An Ontology-Based Approach to Security Risk Identification of Container Deployments in OT Contexts - Landeck, Yannick; Balta, Dian; Wimmer, Martin; Knierim, Christian (48th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP ’26), 2026)
In operational technology (OT) contexts, containerised applications often require elevated privileges to access low-level network interfaces or perform administrative tasks such as application monitoring. These privileges reduce the default isolation provided by containers and introduce significant security risks. Security risk identification for OT container deployments is challenged by hybrid IT/OT architectures, fragmented stakeholder knowledge, and continuous system changes. Existing approaches lack reproducibility, interpretability across contexts, and technical integration with deployment artefacts. We propose a model-based approach—implemented as the Container Security Risk Ontology (CSRO)—which integrates five key domains: adversarial behaviour, contextual assumptions, attack scenarios, risk assessment rules, and container security artefacts. Our evaluation of CSRO in a case study demonstrates that the end-to-end formalisation of risk calculation—from artefact to risk level—enables automated and reproducible risk identification. While CSRO currently focuses on technical, container-level treatment measures, its modular and flexible design provides a solid foundation for extending the approach to host-level and organisational risk factors.
Keywords: peng; Security Risk Identification; Software Containers; Operational Technology; Ontology-Based Modelling

Data Protection Vulnerabilities Assessment for Cross-Organizational Consistency Checking in TEE - Zhu, Derui; Bergemann, Sebastian; Sadeghi, Mohammad; Atkinson, Colin; Pretschner, Alexander (Proceedings of the 8th Workshop on Modeling and Simulation of Software-Intensive Systems (MSSiS), to be published, 2026)
Consistency checking—verifying that development artifacts are free of contradictions—is vital for designing complex and safety-critical cyber-physical systems (CPS). However, CPS development often involves multiple organizations sharing proprietary artifacts, such as partial models and design specifications, which raises concerns about information leakage. Trusted Execution Environments (TEEs) enable confidential computation across organizations without revealing plaintext data. However, side-channel attacks, implicit trust assumptions, and cross-organizational inference can still leak confidential information. This work analyzes confidentiality vulnerabilities in TEE-based cross-organizational consistency checking. We examine practical threat models and identify potential leakages throughout data provisioning, rule execution, and result sharing. To support systematic evaluation, we introduce DataGen, a technique that uses large language models to generate consistency rules and CPS artifacts across diverse application domains. Using DataGen, we benchmark data leakage risks in different TEE settings and highlight overlooked gaps in current protection mechanisms.
Keywords: Consistency management; collaborative model-based systems engineering; confidentiality preservation; Trusted Execution Environment; Model-based Systems Engineering; MBSE

Komplexitätsbeherrschung und Aufwandsreduktion in der Avioniksystementwicklung - Munaro, Tiziano; Schweiger, Andreas; Voss, Sebastian; Eder, Johannes; Barner, Simon (Informatik Spektrum, 2026)
Als Voraussetzung für die Musterzulassung eines Luftfahrzeugs unterliegt die Avioniksystementwicklung strengen behördlichen Regularien. Mit geeigneten Prozessvorgaben wird erreicht, dass ein Flugzeug die dafür erforderliche Betriebssicherheit besitzt. Allerdings entsteht dadurch ein hoher Entwicklungsaufwand, der durch die permanent steigende Systemkomplexität verschärft wird. Deshalb sind geeignete Methoden erforderlich, um in diesem Kontext die gewünschte Produktqualität bei beherrschbaren Kosten gewährleisten zu können. Im vorliegenden Beitrag werden als Beispiele für aktuelle Herausforderungen in der Entwicklung neben der Produktkomplexität die bisher manuell durchgeführten Tätigkeiten zur Allokation von Softwarekomponenten auf Hardwareelemente aufgegriffen. Letztere sind besonders für die Migration bisher monolithischer Software auf Mehrkernprozessoren relevant, womit allerdings hoher Arbeitsaufwand, Fehleranfälligkeit und Qualitätsunsicherheit verbunden sind. Als Lösung wird ein modellbasierter Ansatz vorgeschlagen, welcher durch Einführung von semantisch reichhaltigen, domänenspezifischen Modellen („viewpoints“) eine übersichtliche Darstellung und effiziente Bearbeitung selbst komplexer Systeme ermöglicht und durch die formale Untermauerung für hohes Automatisierungspotenzial (etwa zur Prüfung oder Synthese von Artefakten) sorgt. So wird am Beispiel des Allokationsproblems demonstriert, wie, basierend auf den eingeführten „viewpoints“, existierende Explorationskonzepte genutzt werden können, um automatisch fehlerfreie und optimale Lösungen für komplexe Probleme zu identifizieren. Der Transfer in die industrielle Praxis wird durch eine prototypische Implementierung eines Werkzeugs beschrieben. Abgeschlossen wird der Beitrag mit Vorschlägen für weitere Arbeiten.
Keywords: Model-based Systems Engineering; MBSE; AutoFOCUS3; design-space exploration; DSE

Generative Adversarial Synthetic Super-Resolution for Satellite Based Solar Panels Mapping - Mourabiti, Ziyad; Matar, Jessy; Duchon, Markus (IEEE ACDSA 2026 Conference, 2026)
The widespread adoption of photovoltaic (PV) systems calls for scalable and cost-effective monitoring solutions. While high-resolution aerial imagery enables accurate detection, its limited coverage and high acquisition costs hinder large-scale use. In contrast, low-resolution satellite imagery like Sentinel-2 is freely available but lacks sufficient spatial detail for reliable PV segmentation. This study explores the use of generative super-resolution (SR) techniques to enhance Sentinel-2 imagery and improve downstream PV detection. Three SR models; Real-ESRGAN, Satlas, and S2DR3 were evaluated on diverse German PV sites. Enhanced outputs were assessed using perceptual quality metrics and segmentation performance with a DeepLabV3+ model. Results show that SR significantly improves segmentation accuracy, even with a lightweight model, making it practical for large-scale applications. Although high perceptual quality did not always correlate with better detection, Real-ESRGAN consistently yielded strong segmentation results scoring an IoU higher than 0.90 for certain cases. The findings emphasize the importance of task-specific evaluation in SR applications. Enhanced Sentinel-2 imagery generalizes to other remote sensing tasks that benefit from increased spatial detail, such as land use mapping and infrastructure monitoring.
Keywords: Super-Resolution; Photovoltaic Detection; Satellite Imagery; Generative Models; Remote Sensing

Analyzing the Availability of E-Mail Addresses for PyPI Libraries - Tsakpinis, Alexandros; Pretschner, Alexander (arXiv preprint (under review), 2026)
Open Source Software (OSS) libraries form the backbone of modern software systems, yet their long-term sustainability often depends on maintainers being reachable for support, coordination, and security reporting. In this paper, we empirically analyze the availability of contact information - specifically e-mail addresses - across 686,034 Python libraries on the Python Package Index (PyPI) and their associated GitHub repositories. We examine how and where maintainers provide this information, assess its validity, and explore coverage across individual libraries and their dependency chains. Our findings show that 81.6% of libraries include at least one valid e-mail address, with PyPI serving as the primary source (79.5%). When analyzing dependency chains, we observe that up to 97.8% of direct and 97.7% of transitive dependencies provide valid contact information. At the same time, we identify over 698,000 invalid entries, primarily due to missing fields. These results demonstrate strong maintainer reachability across the ecosystem, while highlighting opportunities for improvement - such as offering clearer guidance to maintainers during the packaging process and introducing opt-in validation mechanisms for existing e-mail addresses.

Feature-Aware Test Generation for Deep Learning Models - Chen, Xingcheng; Weißl, Oliver; Stocco, Andrea (arXiv preprint (under submission), 2026)
As deep learning models are widely used in software systems, test generation plays a crucial role in assessing the quality of such models before deployment. To date, the most advanced test generators rely on generative AI to synthesize inputs; however, these approaches remain limited in providing semantic insight into the causes of misbehaviours and in offering fine-grained semantic controllability over the generated inputs. In this paper, we introduce Detect, a feature-aware test generation framework for vision-based deep learning (DL) models that systematically generates inputs by perturbing disentangled semantic attributes within the latent space. Detect perturbs individual latent features in a controlled way and observes how these changes affect the model’s output. Through this process, it identifies which features lead to behavior shifts and uses a vision-language model for semantic attribution. By distinguishing between task-relevant and irrelevant features, Detect applies feature-aware perturbations targeted for both generalization and robustness. Empirical results across image classification and detection tasks show that Detect generates high-quality test cases with fine-grained control, reveals distinct shortcut behaviors across model architectures (convolutional and transformer-based), and bugs that are not captured by accuracy metrics. Specifically, Detect outperforms a state-of-the-art test generator in decision boundary discovery and a leading spurious feature localization method in identifying robustness failures. Our findings show that fully fine-tuned convolutional models are prone to overfitting on localized cues, such as co-occurring visual traits, while weakly supervised transformers tend to rely on global features, such as environmental variances. These findings highlight the value of interpretable and feature-aware testing in improving DL model reliability.

Forecasting the Maintained Score from the OpenSSF Scorecard for GitHub Repositories linked to PyPI libraries - Tsakpinis, Alexandros; Ergülec, Efe Berk; Schwenger, Emil; Pretschner, Alexander (arXiv preprint (under review), 2026)
The OpenSSF Scorecard is widely used to assess the security posture of open-source software repositories, with the Maintained metric serving as a key indicator of recent maintenance activities, helping users identify actively maintained projects and potentially abandoned dependencies. However, the metric is inherently retrospective, providing only a short-term snapshot based on the past 90 days of repository activity and offering no insight into the future. This limitation complicates risk assessment for developers and organizations that rely on open-source dependencies. In this paper, we investigate the feasibility of forecasting future maintenance activities as captured by the OpenSSF Maintained score. Focusing on 3,220 GitHub repositories linked to one of the top 1% most central PyPI libraries, as ranked by PageRank, we reconstruct historical Maintained scores over a three-year period and frame the problem as a multivariate time series forecasting task. We study four target representations: the raw Maintained score, a bucketed score capturing low, moderate, and high maintenance levels, the numerical trend slope between consecutive scores, and categorical trend types (downward, stable, upward). We compare a statistical model (VARMA), a machine learning model (Random Forest), and a deep learning model (LSTM) using training windows of 3–12 months and forecasting horizons of 1–6 months. Our results show that future maintenance activity can be forecasted with meaningful accuracy, particularly when using aggregated representations such as bucketed scores and trend types leading to accuracies above 0.95 and 0.80. Notably, simpler statistical and machine learning models perform at least on par with deep learning approaches, suggesting that effective forecasting does not require complex architectures. These findings demonstrate that predictive modeling can complement existing Scorecard metrics, enabling more proactive assessment of maintenance activities in open-source software ecosystems.

HyperNet-Adaptation for Diffusion-Based Test Case Generation - Weißl, Oliver; Riccio, Vincenzo; Kacianka, Severin; Stocco, Andrea (arXiv preprint (under submission), 2026)
The increasing deployment of deep learning systems requires systematic evaluation of their reliability in real-world scenarios. Traditional gradient-based adversarial attacks introduce small perturbations that rarely correspond to realistic failures and mainly assess robustness rather than functional behavior. Generative test generation methods offer an alternative but are often limited to simple datasets or constrained input domains. Although diffusion models enable high-fidelity image synthesis, their computational cost and limited controllability restrict their applicability to large-scale testing. We present HyNeA, a generative testing method that enables direct and efficient control over diffusion-based generation. HyNeA provides dataset-free controllability through hypernetworks, allowing targeted manipulation of the generative process without relying on architecture-specific conditioning mechanisms or dataset-driven adaptations such as fine-tuning. HyNeA employs a distinct training strategy that supports instance-level tuning to identify failure-inducing test cases without requiring datasets that explicitly contain examples of similar failures. This approach enables the targeted generation of realistic failure cases at substantially lower computational cost than search-based methods. Experimental results show that HyNeA improves controllability and test diversity compared to existing generative test generators and generalizes to domains where failure-labeled training data is unavailable.

Why Authors and Maintainers Link (or Don’t Link) Their PyPI Libraries to Code Repositories and Donation Platforms - Tsakpinis, Alexandros; Raube, Nicolas; Pretschner, Alexander (arXiv preprint (under review), 2026)
Metadata of libraries on the Python Package Index (PyPI)—including links to source code repositories and donation platforms—plays a critical role in supporting the transparency, trust, and sustainability of open-source libraries. Yet, many packages lack such metadata, and little is known about the underlying reasons. This paper presents a large-scale empirical study combining two targeted surveys sent to 50,000 PyPI authors and maintainers. We analyze more than 1,400 responses using large language model (LLM)-based topic modeling to uncover key motivations and barriers related to linking repositories and donation platforms. While repository URLs are often linked to foster collaboration, increase transparency, and enable issue tracking, some maintainers omit them due to oversight, laziness, or the perceived irrelevance to their project. Donation platform links are reported to support open source work or receive financial contributions, but are hindered by skepticism, technical friction, and organizational constraints. Cross-cutting challenges— such as outdated links, lack of awareness, and unclear guidance— affect both types of metadata. We further assess the robustness of our topic modeling pipeline across 30 runs (84% lexical and 89% semantic similarity) and validate topic quality with 23 expert raters (Randolph’s kappa = 0.55). The study contributes empirical insights into PyPI’s metadata practices and provides recommendations for improving them, while also demonstrating the effectiveness of our topic modeling approach for analyzing short-text survey responses.

A CODECO Case Study and Initial Validation for Edge Orchestration of Autonomous Mobile Robots - Zhu, Hongyu; Samizadeh, Tina; C. Sofia, Rute (IEEE Computer, 2025)
Autonomous Mobile Robots (AMRs) increasingly adopt containerized micro-services across the Edge-Cloud continuum. While Kubernetes is the de-facto orchestrator for such systems, its assumptions of stable networks, homogeneous resources, and ample compute capacity do not fully hold in mobile, resource-constrained robotic environments. This paper describes a case study on smart-manufacturing AMRs and performs an initial comparison between CODECO orchestration and standard Kubernetes using a controlled KinD environment. Metrics include pod deployment and deletion times, CPU and memory usage, and inter-pod data rates. The observed results indicate that CODECO offers reduced CPU consumption and more stable communication patterns, at the cost of modest memory overhead (10-15%) and slightly increased pod lifecycle latency due to secure overlay initialization.
Keywords: CODECO Kubernetes ROS IIoT

Energy-6G Convergence: a Perspective - C. Sofia, Rute; Samovich, Natalie (AIOTI White Paper, Working Group Energy, 2025)
The energy sector is undergoing a profound transformation driven by decarbonization, electrification, decentralization, and digitalization. In parallel, 6G is emerging as the next-generation communication paradigm, envisioned not merely as a faster network, but as an intelligent, sustainable, and human-centric digital infrastructure. This white paper examines the convergence of these two transitions and argues that 6G can become a foundational enabler of future energy systems only if its capabilities are aligned with the safety-critical, long-lived, and sustainability-driven nature of energy infrastructures. In this paper, the following aspects are covered: Introduction: Introduces 6G’s evolutionary and revolutionary features, emphasising its importance for the energy sector. Relevant 6G Features in Energy Systems: Details critical 6G capabilities like heterogeneous networks from edge to cloud, ultra-low latency, AI-driven automation, real-time control, and secure infrastructure all vital for energy systems, and their role in future energy systems and assets. Application Areas: Discusses practical applications of 6G in applications such as smart grids, smart buildings, and industrial energy management. KPIs and Requirements: Summarize quantitative Key Performance Indicators (KPIs) and, increasingly, Key Value Indicators (KVIs) that capture not only technical performance but also sustainability and resilience ones. Challenges and Future Perspectives: Examines issues related to energy efficiency, sustainability, cybersecurity, deployment feasibility, and the technical complexities of converging 6G with energy systems. Conclusions and Road Ahead: Summarises key insights and outlines future steps for leveraging 6G in transforming the energy landscape. A central conclusion of this paper is that observability, security, and sustainability are pre-requisites for trust. Without standardized energy and carbon metrics that link communication services to measurable energy-system outcomes, the contribution of 6G to the energy transition cannot be credibly assessed. Likewise, security-by-design and resilience-by-design are essential as connectivity becomes embedded in critical energy operations. Consequently, 6G must evolve in coexistence with legacy systems, through phased deployment and rigorous validation, rather than disruptive replacement. If energy and digital infrastructures evolve independently, Europe risks inefficiencies, fragility, and loss of technological sovereignty. Conversely, coordinated evolution positions Europe as a global leader in sustainable, trustworthy cyber-physical systems. Hence, debating and addressing the convergence of 6G and energy systems presents a strategic opportunity for Europe to strengthen leadership in sustainable digital infrastructure, critical energy resilience, and trustworthy AI-enabled automation if research, industry, policy, and standardization communities act in a coordinated manner.
Keywords: IIoT Energy 6G

Should AI be Recognized as an Intergenerational Civil Right? - Crowcroft, Jon; C. Sofia, Rute; Trossen, Dirk; Tsaoussidis, Vassilis (axiv pre-print (under submission), 2025)
Artificial Intelligence (AI) is rapidly becoming a foundational layer of social, economic, and cognitive infrastructure. At the same time, the training and large-scale deployment of AI systems rely on finite and unevenly distributed energy, networking, and computational resources. This tension exposes a largely unexamined problem in current AI governance: while expanding access to AI is essential for social inclusion and equal opportunity, unconstrained growth in AI use risks unsustainable resource consumption, whereas restricting access threatens to entrench inequality and undermine basic rights. This paper argues that access to AI outputs largely derived from publicly produced knowledge should not be treated solely as a commercial service, but as a fundamental civil interest requiring explicit protection. We show that existing regulatory frameworks largely ignore the coupling between equitable access and resource constraints, leaving critical questions of fairness, sustainability, and long-term societal impact unresolved. To address this gap, we propose recognizing access to AI as an Intergenerational Civil Right, establishing a legal and ethical framework that simultaneously safeguards present-day inclusion and the rights of future generations. Beyond normative analysis, we explore how this principle can be technically realized. Drawing on emerging paradigms in IoT–Edge–Cloud computing, decentralized inference, and energy-aware networking, we outline technological trajectories and a strawman architecture for AI Delivery Networks that support equitable access under strict resource constraints. By framing AI as a shared social infrastructure rather than a discretionary market commodity, this work connects governance principles with concrete system design choices, offering a pathway toward AI deployment that is both socially just and environmentally sustainable.
Keywords: AI civil rights energy-aware computing AI governance networked AI infrastructures

XMutant: XAI-based Fuzzing for Deep Learning Systems - Chen, Xingcheng; Biagiola, Matteo; Riccio, Vincenzo; d'Amorim, Marcelo; Stocco, Andrea (Empirical Software Engineering, 2025)

Experimenting with Energy-Awareness in Edge-Cloud Containerized Application Orchestration - Ali, Dalal; C. Sofia, Rute (arxiv pre-print (under submission), 2025)
This paper explores the role of energy-awareness strategies into the deployment of applications across heterogeneous Edge-Cloud infrastructures. It proposes methods to inject into existing scheduling approaches energy metrics at a computational and network level, to optimize resource allocation and reduce energy consumption. The proposed approach is experimentally evaluated using a real-world testbed based on ARM devices, comparing energy consumption and workload distribution against standard Kubernetes scheduling. Results demonstrate consistent improvements in energy efficiency, particularly under high-load scenarios, highlighting the potential of incorporating energy-awareness into orchestration processes for more sustainable cloud-native computing.
Keywords: Kubernetes CODECO IIoT Energy-awareness

Knowledge-Augmented Security Risk Identification for OT Container Deployments - Landeck, Yannick; Bueno Momčilović, Tomas; Balta, Dian; Wimmer, Martin; Knierim, Christian (24th International Semantic Web Conference: Companion Volume (ISWC-C 2025), 2025)
Container deployments in operational technology (OT) environments pose unique security challenges, especially when privileged configurations are used. Traditional risk identification methods often fall short in addressing the complexity, dynamic nature, and interdisciplinary collaboration required in these settings. We propose a knowledge augmentation approach that combines semantic modelling, automated reasoning, and tool support to enhance security risk identification. Our approach is demonstrated through an industrial case study, highlighting its practical application. We also examine how large language models (LLMs) can support the instantiation and integration of the approach, improving usability and scalability.
Keywords: peng; Security Risk Assessment; Container Security; Knowledge Augmentation; Operational Technology

Adaptive Learning Paths for Industry 4.0 Competencies: Didactic Framework with Role- and Maturity-Based Differentiation Using the Asset Administration Shell - Büttner, Daniel; Schöttke, Dirk; Schäfer, Stephan; Knoch, Sönke; Porta, Daniel; Schwartz, Tim; Röhricht, Claudette Ocando; Zielstorff, Aaron; Bayha, Andreas (International Conference on Innovative Intelligent Industrial Production and Logistics, 2025)
Keywords: AAS; Asset Administration Shell; Model-based Systems Engineering; MBSE

Mind the Leak: Formalizing Confidentiality Preservation Assessment of Multi-Model Consistency Checking Systems - Bergemann, Sebastian; Bayha, Andreas; Zhu, Derui; Sadeghi, Mohammad; Atkinson, Colin; Pretschner, Alexander (ACM/IEEE 28th International Conference on Model Driven Engineering Languages and Systems (MODELS Companion ’25), 2025)
Ensuring confidentiality during multi-model consistency checking is a critical challenge in collaborative systems engineering. However, it is not yet clear how to assess and compare current and future solutions for multi-model consistency checking with regarding to confidentiality. Therefore, this paper introduces a formalized system model for confidentiality-preserving consistency checking. A formalization of the confidentiality preservation capability of such a system model is proposed to assess whether a given consistency checking system prevents unauthorized information leakage under specific assumptions. Based on these definitions and formalizations, we present an assessment method where an abstract system model is derived from either an implemented or conceptualized consistency checking system, and our confidentiality formalization is applied to assess the system's guarantees for model data confidentiality. Our approach provides system and software engineers with a structured method to assess the confidentiality preservation capability regarding model data in their multi-model consistency checking systems, helping them to identify potential weaknesses and guiding improvements to enhance confidentiality where needed. To demonstrate the applicability of our framework, we apply it to an existing prototype of a partly confidentiality-preserving consistency checking system, as well as two improved versions, assessing their confidentiality preservation strengths and areas for improvement.
Keywords: consistency management; multi-model consistency checking; collaborative model-based systems engineering; confidentiality preservation; Model-based Systems Engineering; MBSE

Spatial Correlation and Graph Neural Networks for Earth Fault Localisation on Energy Grids - Bhattacharya, Somesh; Amaya, Lana; Martinez, Daniel; Eichelseder, Max; Duchon, Markus; Schwefel, Hans-Peter (2025 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe), 2025)
Earth Faults are a common occurrence on Distribution Power Grids, and determining the exact fault location in a compensated MV grid structure is a cumbersome process, which can negatively impact grid reliability. It is imperative for the operators in this scenario to find out fast where an earth fault has occured on the grid. This paper proposes a correlation-based earth fault localisation (EFL), which makes a spatial comparision of stored RMS Voltage signature database from distributed measurement points with obtained measurements. Furthermore, we present another RMS-EFL approach based on Gated Graph Neural Networks, which consider the topological structure of the grid as an input to the model. The assessment of both approaches in a real MV grid topology with simulated faults shows that the proposed methods can provide accurate results and that those are robust with respect to parameters that are frequently hard to obtain in practical deployments.

Targeted Deep Learning System Boundary Testing - Weißl, Oliver; Abdellatif, Amr; Chen, Xingcheng; Merabishvili, Giorgi; Riccio, Vincenzo; Kacianka, Severin; Stocco, Andrea (ACM Transactions on Software Engineering and Methodology, 2025)
Evaluating the behavioral boundaries of deep learning (DL) systems is crucial for understanding their reliability across diverse, unseen inputs. Existing solutions fall short as they rely on untargeted, random perturbations with limited controlled input variations. In this work, we introduce Mimicry, a novel black-box test generator for fine-grained, targeted exploration of DL system boundaries. Mimicry performs boundary testing by leveraging the probabilistic nature of DL outputs to identify promising directions for exploration. By using style-based GANs to disentangle inputs into content and style components, Mimicry generates boundary test inputs by mimicking features from both source and target classes. We evaluated Mimicry’s effectiveness in generating boundary inputs for five DL image classification systems, comparing it to two baselines from the literature. Our results show that Mimicry consistently identifies inputs up to 25× closer to the theoretical decision boundary, outperforming the baselines with statistical significance. Moreover, it generates semantically meaningful boundary test cases that reveal new functional misbehaviors, while the baselines mostly produce corrupted or invalid inputs. Thanks to its enhanced control over latent space manipulations, Mimicry remains effective as dataset complexity grows, resulting in a up to 36% higher validity rate and competitive diversity, as supported by a comprehensive human assessment.

Towards Transferable Deep Reinforcement Learning Policies with high Post-Deployment Performance in Smart Grids - Sebastian Cubides-Herrera, Cristian; Amaya, Lana; Duchon, Markus (2025 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe), 2025)
Reinforcement learning controllers trained in ideal smart-grid simulators fail to operate on real networks due to mismatches in underlying operational dynamics, e.g. battery efficiencies are often neglected or some charge sensors introduce random noise and biases. Our proposed two-layer transfer learning framework bridges this gap without requiring prior knowledge of the physical system (model-agnostic approach). We use an on-the-run trainable domain classifier, which reshapes the reward function to discourage actions that lead to unlikely situations in the target domain while training, and thus, steering learning towards optimal behaviors valid for the real domain. In parallel, a recurrent denoising autoencoder, trained with offline data extracted from scheduled operation, eliminates systematic and stochastic errors from the observation space before control decisions are made. The method was tested on a microgrid system, composed by a photovoltaic module, an electrical load and a battery, under three different efficiencies conditions and 10 random seeds. Our combined method achieved energy cost savings of up to 2.6% when the real efficiency is 80%, 17.5% for efficiency 65% and 10.2% for 50%, while maintaining safety voltage limits in the system. The method shows reliable simulation-to-reality transfer for energy networks proved by statistical significance tests. The overall transfer learning approach was designed to be extendable to grids with more nodes and different optimization objectives.

A Systematic Approach to Fault Injection Test Case Generation in Practice - Munaro, Tiziano; Turalija, Matko; Barner, Simon; Halak, Marko (Software Engineering and Advanced Applications. SEAA 2025., 2025)
The same failure mode, injected into the same component of the same Cyber-Physical System (CPS) may or may not reveal a residual fault depending only on the parametrization of the tested failure scenario: This is shown in Part 10, Clause 8 of the ISO 26262 standard. Hence, when performing Fault Injection (FI) testing to assess the functional correctness, accuracy, and timing of safety mechanisms, it is essential to determine the “worst-case” scenarios in a typically high-dimensional, unbounded, and continuous parameter space. These scenarios are commonly influenced by intricate interactions among system components and between the system and its environment, and are thus known to change with each modification to the system under test. As a result, manually created FI test suites tend to be error-prone, sub-optimal, and expensive to maintain. In contrast, automated FI Test Case Generation (TCG) approaches have already proven to be effective in identifying potentially unsafe behavior of CPS while significantly reducing manual effort. However, given the lack of systematic methodologies for the application of such techniques in real-world development environments, we set to understand how FI TCG can be applied in continuous development, integration, and testing processes. Based on our findings, we propose a methodology for encoding the generation of “worst-case” FI test cases as an optimization problem to which generic optimization approaches can be applied. We apply this methodology to assess two of the safety mechanisms safeguarding the TTTech Auto MotionWise automotive middleware. Using search-based TCG in a Hardware-in-the-Loop (HiL) setup, we demonstrate how it supports the identification of highly challenging FI test cases and increases confidence in the absence of residual faults with minimal manual effort.
Keywords: Software and system safety; Fault injection; Test generation; Continuous development; Model-based Systems Engineering; MbSE

Bridging the Qualification Gap for the Asset Administration Shell: A Modular and Role-Based Learning Framework - Büttner, Daniel; Schöttke, Dirk; Schäfer, Stephan; Knoch, Sönke; Porta, Daniel; Schwartz, Tim; Röhricht, Claudette Ocando; Zielstorff, Aaron; Bayha, Andreas (2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA), 2025)
Keywords: Asset Administration Shell; modular learning; role-based learning; Industry 4.0; Model-based Systems Engineering; MbSE

Building a European-Level Energy Registry - Faraji Shoyari, Mahsa; Matar, Jessy; Duchon, Markus (36th Forum Bauinformatik 2025, 2025)
The increasing integration of renewable energy across Europe demands a scalable and harmonized digital infrastructure to manage large volumes of energy-related data. This paper introduces the concept of a European Energy Registry (EER), aimed at establishing a framework that connects data providers, ensures semantic and structural integration of heterogeneous datasets, and exposes validated datasets to users. We examine the technical and regulatory challenges in developing such a platform and outline a pipeline that leverages big data management, machine learning, and secure IT infrastructure. Key components include accurate data registration from diverse stakeholders, verification and correction mechanisms, and a robust, role-based user access system enhanced with multi-factor authentication and General Data Protection Regulation (GDPR) compliant security protocols. An API-driven data access model is proposed to enable seamless retrieval, filtering, and analysis of energy data. Automated tools for validation and cleansing will support data quality and regulatory compliance. The underlying infrastructure will accommodate scalable databases, pipelines, and AI-driven analytics to provide predictive insights. Cloud-native deployment and APIs will ensure high availability, interoperability, and integration with external platforms. By addressing these requirements, the EER will support Europe’s energy transition plan. This work presents a comprehensive technical blueprint for a secure, interoperable, and future-ready energy registry.
Keywords: Energy Registry; Data Management; Machine Learning; Interoperability

Environmental Monitoring of Industrial Environments with TSMatch (Demo) - da Costa Lima, Ricardo; C. Sofia, Rute (inProc. ACM SIGCOMM 2025, 2025)
TSMatch is an open-source Edge-based middleware which facilitates semantic matchmaking between Internet of Things (IoT) devices and IoT services. The primary objective of TSMatch is to automate IoT data exchange by aligning the capabilities of devices with the requirements of services. This demo showcases how TS-Match intelligently connects IoT with compatible services, using semantic reasoning and machine learning.
Keywords: IIoT; Thing to Service Matching; Edge Computing; Machine Learning

Ontology-Based CAD Analysis with LLMs: Natural Language Querying of Boundary Representations - Mittel, Dominik; Perzylo, Alexander (IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2025)
CAD models using a boundary representation (BREP) are widely used across industrial applications. However, extracting and interpreting information from these models typically requires expert knowledge. In this paper, we present a GraphRAG-based system that lowers this barrier by enabling natural language access to detailed geometric and topological information. The approach builds upon an ontological representation of geometries, which is accessible via the semantic query language SPARQL. Large language models are used to translate user questions into formal queries and generate natural language replies, allowing for flexible, domain-aware interaction with the geometric data. The effectiveness of the system is demonstrated through a systematic evaluation using diverse CAD models and a range of natural language queries. Our system empowers domain experts to explore and analyze complex CAD models without requiring expertise in query languages or ontology design. By reducing the complexity of geometric information retrieval, the GraphRAG-based system enables more accessible, automated, and intelligent use of CAD data in industrial applications.
Keywords: peng

Towards Semantic Grasp Planning using Ontology-based Geometry and Capability Models - Bidlingmaier, Florian; Degenhart, Benjamin; Perzylo, Alexander (IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2025)
Robotic manipulation in small and medium-sized enterprises (SMEs) is challenged by high product variability, small lot sizes, and frequent task reconfigurations. Under these conditions, traditional data-driven grasp planning methods are often infeasible due to their reliance on large, annotated datasets and stable object libraries. This paper presents an architecture for ontology-based semantic grasp planning that enables efficient and intuitive manual grasp pose specification without requiring extensive expertise in geometric programming or machine learning. By leveraging semantic representations of object geometries, gripper capabilities, and task-specific constraints, the system supports context-aware grasp candidate generation and prioritization. While the interpretation of the semantic models identifies suitable grasp types and involved geometric faces of objects and grippers, an optimization step generates suitable 6D poses that meet given constraints. As a result, the complexity of manually instructing robot systems for manipulation tasks is reduced and can be efficiently carried out by non-expert users.
Keywords: peng

Reducing Information Overload with Machine Learning in Mobile Pervasive Augmented Reality Systems - Pascoal, Rui; de Almeida, Ana; C. Sofia, Rute (IEEE Access, 2025)
Augmented reality systems in dynamic environments still struggle with the challenge of what information should be displayed at which time. This work focuses on the case of Mobile Pervasive Augmented Reality Systems (MPARS) and their use in dynamic environments such as outdoor sports. An open-source proof-of-concept for a machine learning-based architecture to implement an MPARS on a specific use case of outdoor usage in a sports environment is presented. The new design for the system relies on heuristics that combine technology acceptance indicators, sensing, and information volume criteria to show the user a contextually meaningful subset of information. The information to the user is displayed in close-to-real-time, and the system can adjust and customise to prevent information overload. A first set of experiments was carried out based on end-user preferences to show the feasibility of the proposed system. To provide meaningful feedback, i.e., the right information when needed or wanted, to sports users on their MPARS experience, a predictive model was trained and shown to be able to estimate when information should be displayed to the user.
Keywords: IIoT Mobile Pervasive Augmented Reality System; Machine Learning; Sensing; Context-awareness; Information Modeler Learning; Adaptable system

SpikeClouds: Streaming Spike-Based Processing of LiDAR for Fast and Efficient Object Detection - Neumeier, Michael; Fasfous, Nael; Li, Bing; von Arnim, Axel (Robotics and Automation Letters, 2025)
LiDAR sensors are used to provide three-dimensional information about the environment in many robotics applications. The information, accumulated in 3D point clouds, is first acquired by the sensor and then processed further, which leads to high end-to-end latencies and large memory footprints. Streaming approaches tackle this problem by processing partial point cloud data during scanning of the environment. In contrast to existing work that is limited to power hungry, rotating mechanical scanners, in this letter, we present a streaming method for more efficient scanline-based LiDAR sensors. We process the sequence of scanlines in form of SpikeClouds with a Spiking Neural Network (SNN) backbone and perform 3D object detection from the accumulated information using a Convolutional Neural Network (CNN) head. Our method achieves close to state-of-the-art detection performance on datasets KITTI and JRDB22 while reducing the end-to-end latency by 10% and the average memory footprint by 95% on standard GPU hardware. Additionally, when ported onto neuromorphic hardware, our backbone requires 25× less energy compared to reference backbones. SpikeClouds achieves fast and efficient environmental perception for robotic applications by streaming LiDAR to enable spike-based processing.
Keywords: neuromorphic; lidar; bmw; automotive; ki-nc; kitti

Automated Factual Benchmarking for In-Car Conversational Systems using Large Language Models - Rafael Giebisch,; Ken E. Friedl,; Lev Sorokin,; Stocco, Andrea (Proceedings of the 36th IEEE Intelligent Vehicles Symposium, 2025)
In-car conversational systems bring the promise toimprove the in-vehicle user experience. Modern conversationalsystems are based on Large Language Models (LLMs), whichmakes them prone to errors such as hallucinations, i.e., inaccurate,fictitious, and therefore factually incorrect information.In this paper, we present an LLM-based methodology forthe automatic factual benchmarking of in-car conversationalsystems. We instantiate our methodology with five LLMbasedmethods, leveraging ensembling techniques and diversepersonae to enhance agreement and minimize hallucinations.We use our methodology to evalute CarExpert, an in-carretrieval-augmented conversational question answering system,with respect to the factual correctness to a vehicle’s manual.We produced a novel dataset specifically created for the in-cardomain, and tested our methodology against an expert evaluation.Our results show that the combination of GPT-4 with theInput Output Prompting achieves over 90% factual correctnessagreement rate with expert evaluations, other than being themost efficient approach yielding an average response time of4.5s. Our findings suggest that LLM-based testing constitutesa viable approach for the validation of conversational systemsregarding their factual correctness.

CNI Telco-Cloud Benchmarking Considerations, draft-samizadeh-bmwg-cni-benchmarking-00 - Samizadeh, Tina; Koukis, George; C. Sofia, Rute; Mamatas, Lefteris; Tsaoussidis, Vassilis (IETF BMWG, 2025)
Keywords: IIoT Benchmarking Container Network Interfaces Kubernetes Edge

Energy-aware Differentiated Services (EA-DS), IETF draft-sofia-green-energy-aware-diffserv-00 - C. Sofia, Rute; Ali, Dalal (IETF GREEN, 2025)
This document proposes to extend the Differentiated Services (DiffServ) Quality of Service (QoS) model to support energy-efficient networking. As a first draft, it discusses how such extensions could be done, bringing first examples of energy-efficiency metrics that could be applied to mark traffic, and providing routing applicability examples by interpreting existing or experimental DSCP codepoints to represent not only traditional QoS parameters (e.g., latency, jitter), but also application-level energy sensitivity. By incorporating energy metrics into traffic classification, network devices and orchestrators can make routing and resource allocation decisions that optimize both service performance and energy consumption.
Keywords: IIoT Energy-awareness Quality of Service Networking architectures and protocols

Extraktion von technischen Daten mit LLMs - Extraction of technical data using LLMs - Experience Report and Evaluation based on Asset Administration Shells - Vogel, Julian; Barth, Christian; Bayha, Andreas; Braunisch, Nico; Garmaev, Igor; Ristin, Marko; Grüner, Sten (VDI-Kongress AUTOMATION 2025, 2025)
Keywords: AAS; LLM; Asset Administration Shell; Model-based Systems Engineering; MBSE

Fertility During Learning in Evolutionary Robot Systems - Di Matteo, Jacopo Michele; Weißl, Oliver; Eiben, Agoston (Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 25), 2025)
Robot evolution systems in which bodies and brains evolve in tandem can be significantly improved by extending them with the ability to learn. Technically, this means that 'newborn' robots are given the opportunity to optimize their inherited brain to control the inherited body adequately. Robots are in an underdeveloped 'infant' stage during this learning stage since their brains and fitness are still being improved. An open issue with regard to this infancy period is that of 'fertility': Should the robot be eligible for mating during the learning stage? This paper explores two distinct approaches from the literature, based on the Triangle of Life (TOL) model, where infant robots cannot produce offspring, and the Morphological Innovation Protection (MIP) mechanism, where they can. The main contribution is a new algorithm, TOL with infant fertility (TOL+IF), inspired by MIP. Experimental comparisons with TOL and MIP show that the new method is superior. TOL+IF is successful not only in producing robots with much higher fitness but also in maintaining the population diversity at higher levels and in evolving different interesting morphologies.

White paper: Towards Human-centric and Sustainable 6G Services - the fortiss Research Perspective - C. Sofia, Rute; Shen, Hao; Liu, Yuanting; Kacianka, Severin; Pfeifer, Holger (fortiss White paper, 2025)
As a leading research institute in software-intensive systems, fortiss is actively shaping the vision of Sixth Generation Mobile Communication (6G). Our mission is to ensure that 6G technologies go beyond technical advancements and are aligned with societal needs. fortiss plays a key role in 6G initiatives worldwide, including contributions to standardization bodies and collaborative Research and Development programs. We focus on software-defined, AI-enabled, and sustainable communication services that prioritize human values and long-term impact. 6G will redefine digital connectivity through cognitive intelligence, decentralized orchestration, and sustainability-oriented architectures. As expectations rise for ultra-reliable low-latency communication (URLLC) and personalized digital services, 6G must outperform prior generations. It will rely on AI-native networking, Edge-Cloud resource orchestration, and energy-aware data frameworks, ensuring both technical performance and societal relevance. This white paper presents the fortiss vision for a human-centric, sustainable, and AI-integrated 6G network. It outlines key research domains such as semantic communication, green orchestration, and distributed AI, all linked to societal and technological challenges. The white paper is aimed at researchers, industry experts, policymakers, and developers. It articulates the strategic direction and contributions of fortiss to 6G, emphasizing responsible innovation and interdisciplinary collaboration toward a meaningful 2030 vision.

An ISO 26262-Derived Evaluation Methodology for Automated Fault Injection Test Case Generators - Benkendorf, Nina; Ganahl, Carolin; Munaro, Tiziano (2025 IEEE Intelligent Vehicles Symposium (IV), 2025)
Fault Injection (FI) is a well-established method to assess the effect of failures within elements of a system under test. Where FI test cases can be executed automatically, such as in simulation-based or Hardware-in-the-Loop (HiL) FI, numerous test case generators (TCGs) have been proposed that aim to uncover more ‘critical’ test cases or to accomplish this using fewer resources. However, the evaluations of these approaches do not allow for direct comparisons: Experiments are often not reproducible, metrics are commonly specific to use cases, and key properties, such as test case distribution, are often not captured. Further, the authors are not aware of any suitable comparison frameworks. Hence, to support practitioners in selecting the most suitable FI TCG for their use case, test setup, and individual goal, this work introduces a set of use case-independent metrics derived from the ISO 26262 safety standard and identifies how these metrics can be applied and analyzed to capture decisive characteristics such as the distribution, criticality, and coverage of generated test cases. We incorporate these metrics and analyses in a start-to-finish methodology and provide their implementation as an open-source tool to effectively and reproducibly evaluate TCGs for automated Fl. The evaluation methodology is assessed in a case study with an industry-oriented cyber-physical system, demonstrating its ability to support practitioners in making an informed decision about the TCG providing the most ap-propriate balance of coverage and efficiency for their particular use case.
Keywords: Measurement; Fault diagnosis; Statistical analysis; Intelligent vehicles; ISO Standards; Diversity reception; Cyber-physical systems; Generators; Safety; Standards; Model-based Systems Engineering; MbSE

Analyzing the Usage of Donation Platforms for PyPI Libraries - Tsakpinis, Alexandros; Pretschner, Alexander (EASE '25: Proceedings of the 29th International Conference on Evaluation and Assessment in Software Engineering, 2025)
Software systems extensively rely on open source software (OSS) libraries, which offer numerous benefits but also pose significant risks. These risks arise when vulnerabilities or attacks emerge, and the OSS community fails to address them promptly due to inactivity or lack of resources. Recent research highlights the strong connection between OSS maintenance activities and financial support. To support the sustainability of the OSS ecosystem, it is crucial for library maintainers to register on donation platforms and link these profiles on the library’s project page accordingly. This allows end users and industry initiatives to provide financial support, ensuring maintainers have access to funding streams. However, a comprehensive investigation on the actual usage of donation platforms in OSS ecosystems is currently missing. This descriptive study analyzes the usage of the most common donation platforms in the PyPI ecosystem. For every available PyPI library, we retrieve its assigned URLs, direct dependencies, and, when available, the owner type and additional donation platform links from its GitHub repository. Using the PageRank algorithm, we analyze the ecosystem for different subsets of libraries looking at both the library and dependency chain perspective. Our study provides several empirical insights regarding the adoption of donation platforms within the PyPI ecosystem. We observe that donation platform links are largely omitted from PyPI project pages, with a strong preference for listing such links exclusively on GitHub repositories. Additionally, GitHub Sponsors emerges as the dominant donation platform, though a notable portion of listed links on PyPI are outdated, highlighting the need for automated link verification. Our findings also reveal significant variations in donation platform adoption across individual libraries and dependency chains. While the analysis of individual PyPI libraries exhibit relatively low adoption rates, libraries used as direct and transitive dependencies show a much higher usage of donation platforms. This widespread adoption of donation platforms among dependencies is a positive sign for developers using PyPI libraries, as these libraries actively seek financial support to sustain ongoing maintenance.

Hallucination Detection in LLMs via Beam Search Sampling and Semantic Consistency Analysis - Bouchekir, Radouane; Faghih, Fathiyeh; Beyene, Tewodros (55th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), 2025)
Large Language Models (LLMs) have revolutionized natural language processing, enabling high-quality content generation. However, they remain prone to hallucinations, instances where generated outputs deviate from factual truth. In this paper, we propose a novel approach that integrates Beam Search Sampling (BSS) with Semantic Consistency Analysis to systematically detect factual hallucinations. Our method leverages BSS to generate multiple candidate answers, capturing the model's confidence distribution across different plausible answers. These answers are then clustered based on semantic similarity, and a Natural Language Inference (NLI) model is applied to assess entailment and contradiction relationships. To quantify hallucinations, we introduce a scoring mechanism that combines token probabilities with semantic similarity metrics. Additionally, for cases where BSS produces a single answer, we incorporate a Chain-of-Verification (CoVe) mechanism to perform self-consistency checks. We evaluate our approach using Llama3.8B on the TruthfulQA dataset, achieving a precision of 0.87 and an AUROC of 0.81 for multi-answer generation. For single-answer verification with CoVe, we report a precision of 0.64 and accuracy of 0.71. Our approach outperforms conventional semantic entrony-based methods
Keywords: Large Language Models (LLMs); Hallucination Detection; Beam Search Sampling; Semantic Consistency.

A Case Study on the Application of Digital Twins for Enhancing CPS Operations - Muntean, Irina; Frasheri, Mirgita; Munaro, Tiziano (Proceedings of the First International Workshop on Autonomous Systems Quality Assurance and Prediction with Digital Twins (ASQAP 2025), 2025)
To ensure the availability and reduce the downtime of complex cyber-physical systems across different domains, e.g., agriculture and manufacturing, fault tolerance mechanisms are implemented which are complex in both their development and operation. In addition, cyber-physical systems are often confronted with limited hardware resources or are legacy systems, both often hindering the addition of new functionalities directly on the onboard hardware. Digital Twins can be adopted to offload expensive computations, as well as providing support through fault tolerance mechanisms, thus decreasing costs and operational downtime of cyber-physical systems. In this paper, we show the feasibility of a Digital Twin used for enhancing cyber-physical system operations, specifically through functional augmentation and increased fault tolerance, in an industry-oriented use case.
Keywords: Model-based Systems Engineering; MbSE

Efficient Domain Augmentation for Autonomous Driving Testing Using Diffusion Models - Luciano Baresi,; Davide Yi Xian Hu,; Stocco, Andrea; Paolo Tonella, (Proceedings of the 47th International Conference on Software Engineering, 2025)
Simulation-based testing is widely used to assess the reliability of Autonomous Driving Systems (ADS), but its effectiveness is limited by the operational design domain (ODD) conditions available in such simulators. To address this limitation, in this work, we explore the integration of generative artificial intelligence techniques with physics-based simulators to enhance ADS system-level testing. Our study evaluates the effectiveness and computational overhead of three generative strategies based on diffusion models, namely instruction-editing, inpainting, and inpainting with refinement. Specifically, we assess these techniques’ capabilities to produce augmented simulator-generated images of driving scenarios representing new ODDs. We employ a novel automated detector for invalid inputs based on semantic segmentation to ensure semantic preservation and realism of the neural generated images. We then perform system-level testing to evaluate the ADS’s generalization ability to newly synthesized ODDs. Our findings show that diffusion models help increase the ODD coverage for system-level testing of ADS. Our automated semantic validator achieved a percentage of false positives as low as 3%, retaining the correctness and quality of the generated images for testing. Our approach successfully identified new ADS system failures before real-world testing.
Keywords: autonomous driving systems; deep learning testing; diffusion models; generative ai

AI, Help Me Think—but for Myself: Assisting People in Complex Decision-Making by Providing Different Kinds of Cognitive Support - Reicherts, Leon; Zhang, Zelun Tony; von Oswald, Elisabeth; Liu, Yuanting; Rogers, Yvonne; Hassib, Mariam (Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, 2025)
How can we design AI tools that effectively support human decision-making by complementing and enhancing users’ reasoning processes? Common recommendation-centric approaches face challenges such as inappropriate reliance or a lack of integration with users’ decision-making processes. Here, we explore an alternative interaction model in which the AI outputs build upon users’ own decision-making rationales. We compare this approach, which we call ExtendAI, with a recommendation-based AI. Participants in our mixed-methods user study interacted with both AIs as part of an investment decision-making task. We found that the AIs had different impacts, with ExtendAI integrating better into the decision-making process and people’s own thinking and leading to slightly better outcomes. RecommendAI was able to provide more novel insights while requiring less cognitive effort. We discuss the implications of these and other findings along with three tensions of AI-assisted decision-making which our study revealed.

Augmenting Human Cognition With Generative AI: Lessons From AI-Assisted Decision-Making - Zhang, Zelun Tony; Reicherts, Leon (CHI '25 T4T: Workshop on Tools for Thought—Research and Design for Understanding, Protecting, and Augmenting Human Cognition with Generative AI, 2025)
How can we use generative AI to design tools that augment rather than replace human cognition? In this position paper, we review our own research on AI-assisted decision-making for lessons to learn. We observe that in both AI-assisted decision-making and generative AI, a popular approach is to suggest AI-generated end-to-end solutions to users, which users can then accept, reject, or edit. Alternatively, AI tools could offer more incremental support to help users solve tasks themselves, which we call process-oriented support. We describe findings on the challenges of end-to-end solutions, and how process-oriented support can address them. We also discuss the applicability of these findings to generative AI based on a recent study in which we compared both approaches to assist users in a complex decision-making task with LLMs.

Benchmarking Generative AI Models for Deep Learning Test Input Generation - Maryam, Maryam; Biagiola, Matteo; Riccio, Vincenzo; Stocco, Andrea (Proceedings of the 18th IEEE International Conference on Software Testing, Verification and Validation, 2025)
Test Input Generators (TIGs) are crucial to assess the ability of Deep Learning (DL) image classifiers to provide correct predictions for inputs beyond their training and test sets. Recent advancements in Generative AI (GenAI) models have made them a powerful tool for creating and manipulating synthetic images, although these advancements also imply increasedcomplexity and resource demands for training. In this work, we benchmark and combine different GenAI models with TIGs, assessing their effectiveness, efficiency, and quality of the generated test images, in terms of domain validity and label preservation. We conduct an empirical study involving three different GenAI architectures (VAEs, GANs, Diffusion Models), five classification tasks of increasing complexity, and 364 human evaluations. Our results show that simpler architectures, such as VAEs, are sufficient for less complex datasets like MNIST. However, when dealing with feature-rich datasets, such as ImageNet, more sophisticated architectures like Diffusion Models achieve superior performance by generating a higher number of valid, misclassification-inducing inputs.
Keywords: Software Testing; Generative AI; Deep Learning

Improved Machine Learning Hybrid Approach For Advanced Solar PV Power Forecasting - Matar, Jessy; Elsherif, Ahmed; Duchon, Markus (IEEE, 2025)
Accurate forecasting of solar photovoltaic (PV) power generation is essential to optimize grid operations, improve energy management, and ensure the stability of renewable energy systems. In this study, we present a new improved hybrid machine learning approach to advance PV power forecasting. The proposed method integrates multiple machine learning models, including ensemble techniques and deep learning, to capture both short-term variability and long-term trends in solar power generation, making use of weather predictions to select the optimal forecasting model. By incorporating weather data, historical power outputs, and advanced feature selection, the model significantly improves the accuracy of power prediction. Through extensive testing on real-world photovoltaic datasets, our approach outperforms conventional forecasting techniques in terms of precision and reliability. The improved hybrid model’s enhanced accuracy offers valuable insights for grid operators and energy planners, contributing to more efficient integration of solar power into the energy grid.
Keywords: PV; Forecasting; Machine Learning; Power; Accuracy

Benchmarking Image Perturbations for Testing Automated Driving Assistance Systems - Lambertenghi, Stefano Carlo; Leonhard, Hannes; Stocco, Andrea (Proc. ICST 2025, 2025)
Advanced Driver Assistance Systems (ADAS) based on deep neural networks (DNNs) are widely used in autonomous vehicles for critical perception tasks such as object detection, semantic segmentation, and lane recognition. However, these systems are highly sensitive to input variations, such as noise and changes in lighting, which can compromise their effectiveness and potentially lead to safety-critical failures. This study offers a comprehensive empirical evaluation of image perturbations, techniques commonly used to assess the robustness of DNNs, to validate and improve the robustness and generalization of ADAS perception systems. We first conducted a systematic review of the literature, identifying 38 categories of perturbations. Next, we evaluated their effectiveness in revealing failures in two different ADAS, both at the component and at the system level. Finally, we explored the use of perturbation- based data augmentation and continuous learning strategies to improve ADAS adaptation to new operational design domains. Our results demonstrate that all categories of image perturbations successfully expose robustness issues in ADAS and that the use of dataset augmentation and continuous learning significantly improves ADAS performance in novel, unseen environments.
IEEE Computer Society TCSE Distinguished Paper Award
Keywords: autonomous driving systems testing; robustness

Exploring the Potential of Freely Available Satellite Data for Energy Applications - Matar, Jessy; Koubaa, Mahdi; Duchon, Markus (2nd NFDI4Energy Conference 2025, 2025)
The growing demand for sustainable energy solutions has highlighted the importance of advanced data-driven decision-making in energy research. Satellite remote sensing offers a transformative opportunity to address this need. Freely accessible satellite datasets, such as those from the Sentinel-2 mission under the Copernicus program, provide immense capabilities for monitoring Earth's surface with high spatial, spectral, and temporal resolutions. Designed for environmental monitoring, Sentinel-2 data enable diverse applications, including renewable energy planning, infrastructure siting, and environmental impact assessments.
Keywords: Satellite; Energy; Machine learning; Segmentation; PVs

Scalable and Interoperable Data Infrastructure in Energy Domain - Faraji Shoyari, Mahsa; Duchon, Markus (2nd NFDI4Energy Conference 2025, 2025)
With the increasing complexity, variability, and volume of data, it is becoming increasingly necessary to develop data access approaches that are interoperable and scalable. Traditional data infrastructure based on batch ETL processes lacks the adaptability necessary to meet modern demands, resulting in organizations not achieving optimal business intelligence results. The focus has shifted from collecting data in a centralized environment to connecting with distributed sources in real time. By using technologies such as data virtualization, diverse data types can be accessed, governed and integrated seamlessly across a variety of platforms. Using APIs, Semantic models, and Container technologies can simplify energy research data management and allow us to navigate today's dynamic ecosystems effectively.
Keywords: Data Virtualization; Interoperability; Scalability; ETL/ELT; API; Containerization

Model-Based Systems Engineering with the SPES Modeling Language: Challenges – The Industrial View - Bergemann, Sebastian; Koch, Walter (Model-Based Systems Engineering with the SPES Modeling Language, 2025)
Keywords: Model-based Systems Engineering; MbSE; SPES; SysML; SpesML

Model-Based Systems Engineering with the SPES Modeling Language: Usage of the Language Constructs in the SPES Methodology - Bayha, Andreas; Bergemann, Sebastian; Böhm, Wolfgang; Philipps, Jan; Vogelsang, Andreas; Voss, Sebastian (Model-Based Systems Engineering with the SPES Modeling Language, 2025)
Keywords: Model-based Systems Engineering; MbSE; SPES; SysML; SpesML

Multi-Dimensional Design Space Exploration: Interplay of Architectural Design Decisions in Evolving Manufacturing Systems - Terzimehić, Tarik (Johannes Kepler University, 2025)
This doctoral thesis explores the design process for manufacturing systems, which encompasses different architectural design decisions (ADDs), such as deploying software to hardware components, determining shop-floor topology, and planning production tasks for stations. Searching for valid or optimized ADDs is conducted through design space exploration (DSE), a methodology dedicated to finding solutions within a predefined and constrained design space. Frequent production changes in evolving manufacturing systems, which are in the focus of the fourth industrial revolution (Industry 4.0), necessitate frequent re-evaluation and re-synthesis of various ADDs. This increases the already high engineering effort and introduces errors, highlighting the need for automating ADDs. The increasing significance of DSE in currently evolving industrial systems, next to a large landscape of DSE solutions in different domains, stresses the importance of establishing a clear and detailed overview of DSE in the context of Industry 4.0. Moreover, the complex interactions between ADDs call for a multi-dimensional DSE approach to synthesize diverse ADDs together. Integrating these ADDs within industrial automation remains challenging, given an applicability shortage of existing DSE solutions from other domains in the Industry 4.0 domain. To address this challenge, this doctoral thesis investigates ADDs and their interplay within a multi-dimensional DSE for evolving software-intensive manufacturing systems, organized into three parts, each addressing one research question. Following the identified problem, the thesis’ first part presents a literature review of existing DSE approaches in Industry 4.0. The review yields a taxonomy of DSE possibilities across three sub-topics: architectural models, ADDs, and optimization methods. The taxonomy clarifies and classifies DSE in Industry 4.0, improving domain understanding, guiding research positioning, and identifying research gaps. It also helps practitioners by providing a common ground for communication and initial guidance in identifying relevant ADDs, finding correlated ADDs, and selecting suitable architectures and techniques. The second part establishes a novel multi-dimensional DSE approach that uses domain-specific input models and a satisfiability modulo theories (SMT) solver to automatically generate valid and optimal deployment, service composition, shop-floor topology, and production planning ADDs. The applicability of the proposed DSE approach is evaluated on different domain-relevant demonstrators, showcasing reduced engineering effort and improved design quality. The third part introduces mitigation constraints to investigate the synergy and ensure the compatibility of ADDs. This enables the exploration of different sequential and hybrid DSE workflows in addition to a joint DSE workflow. Findings reveal that starting with production planning ADDs yields the best overall performance, though specific optimization goals may favor other workflows (e.g., starting with deployment ADDs to prioritize communication coupling). Sequential workflows starting with topology ADDs are the fastest, while the joint workflow offers the highest solution quality and is easier to implement. This analysis provides additional guidance for software architects on enhancing the quality and efficiency of ADDs. This thesis contributes to manufacturing system design and practice by providing a comprehensive framework to understand and optimize the interplay of various ADDs. This framework encompasses i) an SMT-based multi-dimensional DSE approach for ADD synthesis and optimization, ii) an evaluation of DSE workflows and their performances, iii) a taxonomy of ADDs in Industry 4.0 to guide research and practice, and iv) recommendations for practitioners on optimizing ADDs. Overall, the thesis provides practitioners and researchers in industrial automation with tools to support more effective decision-making in designing and optimizing manufacturing systems.

System Safety Monitoring of Learned Components Using Temporal Metric Forecasting - Sepehr Sharifi,; Stocco, Andrea; Lionel C. Briand, (ACM Transactions on Software Engineering and Methodology, 2025)
In learning-enabled autonomous systems, safety monitoring of learned components is crucial to ensure their outputs do not lead to system safety violations, given the operational context of the system. However, developing a safety monitor for practical deployment in real-world applications is challenging. This is due to limited access to internal workings and training data of the learned component. Furthermore, safety monitors should predict safety violations with low latency, while consuming a reasonable computation resource amount.To address the challenges, we propose a safety monitoring method based on probabilistic time series forecasting. Given the learned component outputs and an operational context, we empirically investigate different Deep Learning (DL)-based probabilistic forecasting to predict the objective measure capturing the satisfaction or violation of a safety requirement (safety metric). We empirically evaluate safety metric and violation prediction accuracy, and inference latency and resource usage of four state-of-the-art models, with varying horizons, using autonomous aviation and autonomous driving case studies. Our results suggest that probabilistic forecasting of safety metrics, given learned component outputs and scenarios, is effective for safety monitoring. Furthermore, for both case studies, the Temporal Fusion Transformer (TFT) was the most accurate model for predicting imminent safety violations, with acceptable latency and resource consumption.

An Equivariant Machine Learning Decoder for 3D Toric Codes - Weißl, Oliver; Egorov, Evgenii (2025 International Conference on Quantum Communications, Networking, and Computing (QCNC), 2025)
Research on mitigating errors in computing and communication systems has grown with their widespread use. In quantum computing, error correction is crucial as errors can quickly propagate, undermining results and the theoretical speedup over classical systems. Quantum error-correcting codes, particularly topological codes, are a key focus. These codes map parity check matrices to graphs on d-dimensional surfaces, with our research centered on the 3D toric code. Effective decoders must be robust to noise, with performance improving as code size increases, but their complexity should scale polynomially with lattice size for practicality. We propose a neural network with inductive bias, leveraging equivariance to generalize efficiently from a smaller input subset. Additionally, we explore transformer networks for error correction and compare these methods to existing techniques for decoding errors in the 3D toric code, highlighting their strengths and limitations.

EEvAct: Early Event-Based Action Recognition with High-Rate Two-Stream Spiking Neural Networks. - Neumeier, Michael; Lecomte, Jules; Kazinski, Nils; Banik, Soubarna; Li, Bing; von Arnim, Axel (Proceedings of the International Conference on Neuromorphic Systems (ICONS), 2025)
Recognizing human activities early is crucial for the safety and responsiveness of human-robot and human-machine interfaces. Due to their high temporal resolution and low latency, event-based vision sensors are a perfect match for this early recognition demand. However, most existing processing approaches accumulate events to low-rate frames or space-time voxels which limits the early prediction capabilities. In contrast, spiking neural networks (SNNs) can process the events at a highrate for early predictions, but most works still fall short on final accuracy. In this work, we introduce a high-rate two-stream SNN which closes this gap by outperforming previous work by 2% in final accuracy on the large-scale THU EACT-50 dataset. We benchmark the SNNs within a novel early event-based recognition framework by reporting Top-1 and Top-5 recognition scores for growing observation time. Finally, we exemplify the impact of these methods on a real-world task of early action triggering for human motion capture in sports.
Keywords: neuromorphic; event-based vision; gesture recognition; edge-ai; low-power ai; tennis; simi; action recognition; spikingbody

Exploring the Use of LLMs for the Selection Phase in Systematic Literature Studies - Thode, Lukas; Iftikhar, Umar; Mendez, Daniel (Information and Software Technology, 2025)
Context: Systematic literature studies, such as secondary studies, are crucial to aggregate evidence. An essential part of these studies is the selection phase of relevant studies. This, however, is time-consuming, resource-intensive, and error-prone as it highly depends on manual labor and domain expertise. The increasing popularity of Large Language Models (LLMs) raises the question to what extent these manual study selection tasks could be supported in an automated manner. Objectives: In this manuscript, we report on our effort to explore and evaluate the use of state-of-the-art LLMs to automate the selection phase in systematic literature studies. Method: We evaluated LLMs for the selection phase using two published systematic literature studies in software engineering as ground truth. Three prompts were designed and applied across five LLMs to the studies’ titles and abstracts based on their inclusion and exclusion criteria. Additionally, we analyzed combining two LLMs to replicate a practical selection phase. We analyzed recall and precision and reflected upon the accuracy of the LLMs, and whether the ground truth studies were conducted by early career scholars or by more advanced ones. Results: Our results show a high average recall of up to 98% combined with a precision of 27% in a single LLM approach and an average recall of 99% with a precision of 27% in a two-model approach replicating a two-reviewer procedure. Further the Llama 2 models showed the highest average recall 98% across all prompt templates and datasets while GPT4-turbo had the highest average precision 72%. Conclusions: Our results demonstrate how LLMs could support a selection phase in the future. We recommend a two LLM-approach to archive a higher recall. However, we also critically reflect upon how further studies are required using other models and prompts on more datasets to strengthen the confidence in our presented approach.
Keywords: Large language models; Systematic literature studies; Automation

NLP4RE Tools: Classification, Overview and Management - Frattini, Julian; Unterkalmsteiner, Michael; Fucci, Davide; Mendez, Daniel (Handbook on Natural Language Processing for Requirements Engineering, 2025)
Tools constitute an essential contribution to natural language processing for requirements engineering (NLP4RE) research. They are executable instruments that make research usable and applicable in practice. In this chapter, we first introduce a systematic classification of NLP4RE tools. Then, we extend an existing overview with a systematic summary of 126 NLP4RE tools published between April 2019 and June 2023. Finally, we provide instructions on how to create, maintain and disseminate NLP4RE tools. The content of this chapter contributes (1) a classification scheme to improve the understanding of their types and properties, (2) a systematic overview to ease the reuse and evolution of existing tools and (3) guidelines to support a more rigorous management and dissemination.

OpenCat: Improving Interoperability of ADS Testing - Qurban Ali,; Stocco, Andrea; Leonardo Mariani,; Oliviero Riganelli, (Proceedings of the 47th International Conference on Software Engineering Workshops, 2025)

TONUS: Neuromorphic human pose estimation for artistic sound co-creation - Lecomte, Jules; Zinner, Konrad; Neumeier, Michael; von Arnim, Axel (Proceedings of International Joint Conference on Neural Networks (IJCNN), 2025)
Human machine interaction is a huge source of inspiration in today’s media art and digital design, as machines and humans merge together more and more. Its place in art reflects its growing applications in industry, such as robotics. However, those interactions often remains too technical and machine-driven for people to really engage into. On the artistic side, new technologies are often not explored in their full potential and lag a bit behind, so that state-of-the-art research does not make its way up to museums and exhibitions. Machines should support people’s imagination and poetry in a seamless interface to their body or soul. We propose an artistic sound installation featuring neuromorphic body sensing to support a direct yet non intrusive interaction with the visitor with the purpose of creating sound scapes together with the machine. We design a neuromorphic multihead human pose estimation neural sensor that shapes sound scapes and visual output with fine body movement control. In particular, the feature extractor is a spiking neural network tailored for a dedicated neuromorphic chip. The visitor, immersed in a sound atmosphere and a neurally processed representation of themselves that they control, experience the dialogue with a machine that thinks neurally, similarly to them.
Keywords: neuromorphic; event-based vision; art; sound; pose estimation; simi; spikingbody; skeletton reconstruction

Towards Lean Research Inception: Assessing Practical Relevance of Formulated Research Problems - Fernandes Pereira, Anrafel; Kalinowski, Marcos; Baldassarre, Teresa; Börstler, Jürgen; bin Ali, Nauman; Mendez, Daniel (Proc. of the 29th International Conference on Evaluation and Assessment in Software Engineering, 2025)
[Context] The lack of practical relevance in many Software Engineering (SE) research contributions is often rooted in oversimplified views of industrial practice, weak industry connections, and poorly defined research problems. Clear criteria for evaluating SE research problems can help align their value, feasibility, and applicability with industrial needs. [Goal] In this paper, we introduce the Lean Research Inception (LRI) framework, designed to support the formulation and assessment of practically relevant research problems in SE. We describe its initial evaluation strategy conducted in a workshop with a network of SE researchers experienced in industry-academia collaboration and report the evaluation of its three assessment criteria (valuable, feasible, and applicable) regarding their importance and completeness in assessing practical relevance. [Method] We applied LRI retroactively to a published research paper, engaging workshop participants in discussing and assessing the research problem by applying the proposed criteria using a semantic differential scale. Participants provided feedback on the criteria’s importance and completeness, drawn from their own experiences in industry-academia collaboration. [Results] The findings reveal an overall agreement on the importance of the three criteria – valuable (83.3%), feasible (76.2%), and applicable (73.8%) – for aligning research problems with industrial needs. Qualitative feedback suggested adjustments in terminology with a clearer distinction between feasible and applicable, and refinements for valuable by more clearly considering business value, ROI, and originality. [Conclusion] While LRI still constitutes ongoing research and requires further evaluation, our emerging results strengthen our confidence that the three criteria applied using the semantic differential scale can already help the community assess the practical relevance of SE research problems.

Assurance of Application Security on IIoT Platforms with Knowledge Augmentation - Landeck, Yannick; Balta, Dian; Wimmer, Martin; Knierim, Christian (2024 Annual Computer Security Applications Conference Workshops (ACSAC Workshops), 2024)
Industrial Internet of Things (IIoT) platforms are characterised by a heterogeneity of applications that increases the flexibility and efficiency of automated manufacturing. At the same time, manufacturers are concerned whether untrusted third-party applications can be aligned with the domain’s requirements for security. Assurance activities, aiming to guarantee the secure development and operation of applications, are hampered by risk management challenges and the heterogeneity of required knowledge. Stakeholders such as Software Developers and System Integrators struggle to break down these challenges, e.g. how to mitigate threats to Industrial Control Systems (ICS) and derive actions for assurance. Additionally, they require knowledge to manage risks from heterogeneous facets such as application deployment, device configuration, or threat and risk assessment. In this paper, we propose an assurance engine that allows stakeholders to break down risk management challenges into dynamic assurance cases and to augment the latter with knowledge from corresponding facets. By leveraging knowledge representation, the assurance engine enables the comparison of risk management approaches for different stakeholders. Furthermore, we utilise formal semantics and logical deduction for reasoning to lay the grounds for the automated assessment of complex assurance cases on heterogeneous IIoT platforms.
Keywords: peng

Decentralized Optimal Scheduler for Prosumers in a Local Electric Energy Market - Okwuibe, Godwin; Wolf, Armin; Han, Zhiwei (2025 5th International Conference on Smart Grid and Energy Internet (SGEI 2025), 2024)
Local energy markets (LEMs) provide an opportunity for prosumers to exchange their energy at the local grid level. Despite various studies on LEMs and their use in decentralizing the traditional top-down approach of electricity grids, there is still a lack of effective coordination and scheduling methods for energy trading. The concepts still lack ideas as regards how to effectively coordinate the markets and schedule the local devices for proper energy trading. In this work, we propose an open-source optimal scheduling model to enable local prosumers to effectively coordinate their devices for local energy trading. The proposed model was evaluated for its applicability in a German use case scenario. The simulation results show that the model can create a community self-sufficiency of up to 82% if properly coordinated and provide additional economic benefits to the local prosumers.
Keywords: Local energy market; energy trading

Neuromorphic force-control in an industrial task: validating energy and latency benefits - Amaya, Lana; Eames, Evan; Palinauskas, Gintautas; Perzylo, Alexander; Sandamirskaya, Yulia; von Arnim, Axel (IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024)
As robots become smarter and more ubiquitous, optimizing the power consumption of intelligent compute becomes imperative towards ensuring the sustainability of technological advancements. Neuromorphic computing hardware makes use of biologically inspired neural architectures to achieve energy and latency improvements compared to conventional von Neumann computing architectures. Applying these benefits to robots has been demonstrated in several works in the field of neurorobotics, typically on relatively simple control tasks. Here, we introduce an example of neuromorphic computing applied to the real-world industrial task of object insertion. We trained a spiking neural network (SNN) to perform force-torque feedback control using a reinforcement learning approach in simulation. We then ported the SNN to the Intel neuromorphic research chip Loihi interfaced with a KUKA robotic arm. At inference time we show latency competitive with current CPU/GPU architectures, and one order of magnitude less energy usage in comparison to traditional low-energy edge-hardware. We offer this example as a proof of concept implementation of a neuromoprhic controller in a real-world robotic setting, highlighting the benefits of neuromorphic hardware for the development of intelligent controllers for robots.
Keywords: nc; peng

Beyond Recommendations: From Backward to Forward AI Support of Pilots' Decision-Making Process - Zhang, Zelun Tony; Feger, Sebastian S.; Dullenkopf, Lucas; Liao, Rulu; Süsslin, Lou; Liu, Yuanting; Butz, Andreas (Proceedings of the ACM on Human-Computer Interaction, 2024)
AI is anticipated to enhance human decision-making in high-stakes domains like aviation, but adoption is often hindered by challenges such as inappropriate reliance and poor alignment with users' decision-making. Recent research suggests that a core underlying issue is the recommendation-centric design of many AI systems, i.e., they give end-to-end recommendations and ignore the rest of the decision-making process. Alternative support paradigms are rare, and it remains unclear how the few that do exist compare to recommendation-centric support. In this work, we aimed to empirically compare recommendation-centric support to an alternative paradigm, continuous support, in the context of diversions in aviation. We conducted a mixed-methods study with 32 professional pilots in a realistic setting. To ensure the quality of our study scenarios, we conducted a focus group with four additional pilots prior to the study. We found that continuous support can support pilots' decision-making in a forward direction, allowing them to think more beyond the limits of the system and make faster decisions when combined with recommendations, though the forward support can be disrupted. Participants' statements further suggest a shift in design goal away from providing recommendations, to supporting quick information gathering. Our results show ways to design more helpful and effective AI decision support that goes beyond end-to-end recommendations.
Keywords: human-AI interaction; decision support tools; intelligent decision support; AI-assisted decision-making; human-AI decision-making; imperfect AI; decision support paradigms; continuous support; process-oriented support; aviation

Continuous safety & security evidence generation, curation and assurance case construction using the Evidential Tool Bus - Shankar, Natarajan; Kim, Minyoung; Sanchez, Huascar; Rueß, Harald; Beyene, Tewodros; Bouchekir, Radouane; al., et (2024 AIAA DATC/IEEE 43rd Digital Avionics Systems Conference (DASC), 2024)
Establishing assurance of software is indispensable in safety-critical systems. Constructing an assurance case for safety & security of software subsumes the entire development and V&V workflow involving the use of multitude of (formal) analysis tools to develop claims supported by diverse sets of evidence. This evidence needs to be curated for certification and assurance case construction. Further, the complexity of information flows gets compounded due to changing needs & goals over the course of certification. We demonstrate the application of the Evidential Tool Bus (ETB2), on an industrial use case workflow involving several tools and methodologies, to support continuous Evidence Generation, their Curation & Assurance Case Construction, from major industrial collaborators in Aviation. Evidence Generation follows the Design for Certification (DesCert) methodology. Curation uses the Rapid Assurance Curation Kit (RACK) for semantic reification of evidential data. Assurance case tools using, then use the curated evidence from RACK for assurance case construction.
Keywords: Continuous Assurance; Certification Evidence Generation; Certification Evidence Curation

Loss-Driven Systems Engineering und die Tragödie vieler "-ilities" - Krynicki, Thomas; Ernst, Cornelia; Barner, Simon; Emig, Tobias (Tag des Systems Engineering 2024, 2024)
Der vielfach beobachtete Stand der Praxis in der Industrie ist, dass sich eine systematische Berücksichtigung von Systemeigenschaften in der Produktentwicklung auf wenige ausgewählte Eigenschaften fokussiert, wie z.B. Funktionale Sicherheit und Cybersecurity. Weitere Systemeigenschaften wie etwa Skalierbarkeit, Nachhaltigkeit oder Resilienz erfahren jedoch eine immer größere Bedeutung für den Erfolg und die Wirtschaftlichkeit des Produktes. Für viele dieser sogenannten "-ilities", die in der zweiten Reihe zu stehen scheinen, gibt es für gewöhnlich keine standardisierten Vorgehensweisen. Sie werden in der Entwurfsphase ignoriert oder vernachlässigt, oder es werden die damit verbundenen Engineering-Aktivitäten oft getrennt voneinander durchgeführt. In diesem Paper wird vorgestellt, wie mit dem ganzheitlichen Ansatz „Loss-Driven Systems Engineering“ und der Anwendung von etablierten Safety-Engineering-Methoden aus dem Bereich Automotive Synergien bei der Berücksichtigung aller relevanten Systemeigenschaften genutzt werden können. Neben dem konzeptuellen Ansatz werden Lessons Learned aus einer praktischen Anwendung in einem Beispiel vorgestellt, das im Rahmen des GfSE-Workshops 2024 untersucht wurde.
Keywords: Loss-Driven Systems Engineering; Systemeigenschaften; STPA; ISO 26262; Model-based Systems Engineering; MBSE

Measuring the effect of mental workload and explanations on appropriate AI reliance using EEG - Zhang, Zelun Tony; Ketenci Argın, Seniha; Bilen, Mustafa Baha; Urgun, Doğan; Deniz, Sencer Melih; Liu, Yuanting; Hassib, Mariam (Behavior & Information Technology, 2024)
AI is anticipated to improve human decision-making across various domains, often in high-stakes, difficult tasks. However, human reliance on AI recommendations is often inappropriate. A common approach to address this is to provide explanations about the AI output to decision makers, but results have been mixed so far. It often remains unclear when people can rely appropriately on AI and when explanations can help. In this work, we conducted a lab experiment (N = 34) to investigate how the appropriateness of human reliance on (explainable) AI depends on the mental workload induced by different decision difficulties. Instead of self-assessments, we used EEG (Emotiv Epoc Flex head cap, 32 wet electrodes) to more directly measure participants' mental workload. We found that the difficulty of a decision, indicated by the induced mental workload, strongly influences participants' ability to rely appropriately on AI, as assessed through relative self-reliance, relative AI reliance, and decision accuracy with and without AI. While reliance was appropriate for low mental workload decisions, participants were prone to overreliance in high mental workload decisions. Explanations had no significant effect in either case. Our results imply that alternatives to the common ‘recommend-and-explain’ approach should be explored to assist human decision-making in challenging tasks.
Advance online publication
Keywords: human-AI decision-making; explainable AI; appropriate reliance; decision difficulty; mental workload; EEG

A Platform Ecosystem Providing New Data For The Energy Transition - Duchon, Markus; Matar, Jessy; Faraji Shoyari, Mahsa; Perzylo, Alexander; Kessler, Ingmar; Buchenberg, Patrick; Kuhn, Philipp; Hamacher, Thomas; Suriyamoorthy, Ghayathri; Schlachter, Thorsten; Süss, Wolfgang; Xuan Thinh, Nguyen; et al. (DACH+ Energy Informatics Conference, 2024)
There is a great need for high-quality and comprehensive data in the energy sector. This data is collected and preprocessed at considerable expense and is not only required for research, but also by planning offices and other industries in connection with planning activities, such as the creation of municipal heat planning. The NEED ecosystem will accelerate these processes establishing an efficient, robust, and scalable energy data ecosystem. Heterogeneous energy-related data sources will be brought together and automatically linked consistently across different sectors as well as temporal and spatial levels. In this context, existing data sources will not be replaced but rather integrated into the NEED ecosystem as dedicated sources including a semantic description on how to utilize them. In addition to conventional data sources from the various planning levels, we envision a quality assessment scheme based on the FAIR criteria. In reality, we are often faced with missing data, too. To close this gap we explore data-driven, model-driven, AI-based, and tool-driven generation of synthetic data. These heterogeneous data sources will be interlinked using ontology modules which will be represented in a knowledge graph. Via a semantic API, queries will be generated to identify the required data sources, which will be orchestrated to provide the data needed. This will enable researchers, planners, and others including their tools to interact with the NEED ecosystem, while a tool proxy will be able to translate the resulting data, which often has proprietary formats, required by some tools to operate. The NEED ecosystem is planned to be a robust, easy-to-maintain, and flexible infrastructure to enhance planning energy measures at different spatial levels and with different time horizons, without losing sight of the big picture. We envision to evaluate our NEED approach for the transparent provision of data by integrating relevant data sources, definition and analysis of application scenarios in the planning domain, as well as the integration of various tools for different planning purposes. With these elements, we will be able to quantify the efficiency of data procurement and demonstrate the functionality of the approach using practical use cases.
Keywords: asci; peng; need

Developing Assurance Cases for Adversarial Robustness and Regulatory Compliance in LLMs - Bueno Momčilović, Tomas; Balta, Dian; Buesser, Beat; Zizzo, Giulio; Purcell, Mark (9th International Workshop on Assurance for Software-intensive Systems, 2024)
This paper presents an approach to developing assurance cases for adversarial robustness and regulatory compliance in large language models (LLMs). Focusing on both natural and code language tasks, we explore the vulnerabilities these models face, including adversarial attacks based on jailbreaking, heuristics, and randomization. We propose a layered framework incorporating guardrails at various stages of LLM deployment, aimed at mitigating these attacks and ensuring compliance with the EU AI Act. Our approach includes a meta-layer for dynamic risk management and reasoning, crucial for addressing the evolving nature of LLM vulnerabilities. We illustrate our method with two exemplary assurance cases, highlighting how different contexts demand tailored strategies to ensure robust and compliant AI systems.
Keywords: peng

Knowledge-Augmented Reasoning for EUAIA Compliance and Adversarial Robustness of LLMs - Bueno Momčilović, Tomas; Balta, Dian; Buesser, Beat; Zizzo, Giulio; Purcell, Mark (International Workshop on AI Value Engineering and AI Compliance Mechanisms (VECOMP 2024), 2024)
The EU AI Act (EUAIA) introduces requirements for AI systems which intersect with the processes required to establish adversarial robustness. However, given the ambiguous language of regulation and the dynamicity of adversarial attacks, developers of systems with highly complex models such as LLMs may find their effort to be duplicated without the assurance of having achieved either compliance or robustness. This paper presents a functional architecture that focuses on bridging the two properties, by introducing components with clear reference to their source. Taking the detection layer recommended by the literature, and the reporting layer required by the law, we aim to support developers and auditors with a reasoning layer based on knowledge augmentation (rules, assurance cases, contextual mappings). Our findings demonstrate a novel direction for ensuring LLMs deployed in the EU are both compliant and adversarially robust, which underpin trustworthiness.
Keywords: peng

Knowledge-based Programming by Demonstration using semantic action models for industrial assembly - Ding, Junsheng; Zhang, Haifan; Li, Weihang; Zhou, Liangwei; Perzylo, Alexander (IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024)
In this paper, we introduce a knowledge-based Programming by Demonstration (kb-PbD) paradigm to facilitate robot programming in small and medium-sized enterprises (SMEs). PbD in production scenarios requires the recognition of product-specific actions but faces challenges in the lack of suitable and comprehensive datasets, due to the large variety of involved hand actions across different production scenarios. To address this issue, we utilize standardized grasp types as the fundamental feature to recognize basic hand movements, where a Long Short-Term Memory (LSTM) network is employed to recognize grasp types from hand landmarks. The product-specific actions, aggregated from the basic hand movements, are formally modeled in a semantic description language based on the Web Ontology Language (OWL). Description Logic (DL) is used to define the actions with their characteristic properties, which enables the efficient classification of new action instances by an OWL reasoner. The semantic models of hand actions, robot tasks, and workcell resources are interconnected and stored in a Knowledge Base (KB), which enables the efficient pair-wise translation between hand actions and robot tasks. For the reproduction of human assembly processes, actions are converted to robot tasks via skill descriptions, while reusing the action parameters of involved objects to ensure product integrity. We showcase and evaluate our method in an industrial production setting for control cabinet assembly.
Keywords: peng

Scaling Up Resonate-and-Fire Networks for Fast Deep Learning - Huber, Thomas; Lecomte, Jules; Polovnikov, Borislav; von Arnim, Axel (Springer Nature, 2024)
Spiking neural networks (SNNs) present a promising com- puting paradigm for neuromorphic processing of event-based sensor data. The resonate-and-fire (RF) neuron, in particular, appeals through its biological plausibility, complex dynamics, yet computational simplicity. Despite theoretically predicted benefits, challenges in parameter initial- ization and efficient learning inhibited the implementation of RF net- works, constraining their use to a single layer. In this paper, we address these shortcomings by deriving the RF neuron as a structured state space model (SSM) from the HiPPO framework. We introduce S5-RF, a new SSM layer comprised of RF neurons based on the S5 model, that fea- tures a generic initialization scheme and fast training within a deep ar- chitecture. S5-RF scales for the first time a RF network to a deep SNN with up to four layers and achieves with 78.8% a new state-of-the-art result for recurrent SNNs on the Spiking Speech Commands dataset in under three hours of training time. Moreover, compared to the reference SNNs that solve our benchmarking tasks, it achieves similar performance with much fewer spiking operations. Our code is publicly available at https://github.com/ThomasEHuber/s5-rf.
Keywords: Spiking Neural Networks · Resonate-and-Fire Neuron · State Space Models · Bio-Inspired Computational Methods

A Failure Model Library for Simulation-based Validation of Functional Safety - Munaro, Tiziano; Muntean, Irina; Pretschner, Alexander (Proceedings of the 43rd International Conference on Computer Safety, Reliability and Security (SAFECOMP), 2024)
Simulation-based Fault Injection (FI) is highly recommended by functional safety standards in the automotive and aerospace domains, in order to “support the argumentation of completeness and correctness of a system architectural design with respect to faults” (ISO 26262). We argue that a library of failure models facilitates this process. Such a library, firstly, supports completeness claims through, e.g., an extensive and systematic collection process. Secondly, we argue why failure model specifications should be executable—to be implemented as FI operators within a simulation framework—and parametrizable—to be relevant and accurate for different systems. Given the distributed nature of automotive and aerospace development processes, we moreover argue that a data-flow-based definition allows failure models to be applied to black-box components. Yet, existing sources for failure models provide fragmented, ambiguous, incomplete, and redundant information, often meeting neither of the above requirements. We therefore introduce a library of 18 executable and parameterizable failure models collected with a systematic literature survey focusing on automotive and aerospace Cyber-Physical Systems (CPS). To demonstrate the applicability to simulation-based FI, we implement and apply a selection of failure models to a real-world automotive CPS within a state-of-the-art simulation environment, and highlight their impact.
Keywords: Functional Safety; Failure Models; Simulation-based Fault Injection; Cyber-Physical Systems; MbSE; Model-based Systems Engineering

A Knowledge-Augmented Socio-Technical Assistance System for Product Engineering - Mittel, Dominik; Hubert, Andreas; Srinivasan, Uppili; Perzylo, Alexander; Lemberger, Daniel (IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2024)
Manufacturing companies are exposed to increasingly complex products and shorter product engineering cycles. Unstructured data hinders the integration of knowledge over the different product engineering stages and complicates structured product development. However, combining an integrated view on relevant data sources following the Advanced Product Quality Planning (APQP) approach provides guidance for product engineers. In this paper, a semantic Knowledge Base (KB), a Process Execution System (PES), and a Computer Vision System (CVS) are introduced, which, in their interaction, compose a Socio-Technical Assistance System (STAS). We combine semantic models of production knowledge, APQP-guided product development, and ontology-based geometric representations of products and manufacturing resources. The PES coordinates the interaction with the user and other system components. The CVS tracks used tools and parts during the assembly and, therefore, enables traceability features and creates confidence in the quality of the assembly. As a result, the developed STAS prototype offers support from customer inquiry through product design and development to manufacturing and assembly, as well as after-sales support. The assistance system enables handling of complex products efficiently in order to reduce required times and costs.
Keywords: peng; diprolea

Accessing the Capabilities of KGs and LLMs in Mapping Indicators within Sustainability Reporting Standards - Zhou, Yuchen; Gu, Xin; Ding, Junsheng; Chen, Sirou; Perzylo, Alexander (Workshop on Natural Language Processing for Knowledge Graph Creation (NLP4KGC) at International Conference on Semantic Systems (SEMANTICS), 2024)
Sustainability reporting is gaining importance in response to climate change and the pursuit of social sustainable development. In preparing these reports, sustainability managers are tasked with identifying sustainability indicators across multiple reporting standards. However, the challenge arises to locate the corresponding indicators in another standard through keyword searches due to the inconsistency of the naming conventions and classifications across different standards. Knowledge graphs(KGs) offer a promising solution for mapping the concepts of sustainability reporting from diverse standards. Nonetheless, traditional approaches to construct KGs are often time and resource intensive. In this context, the advanced natural language understanding capabilities of the Large Language Models (LLMs) could be explored to comprehend the reporting standards. Additionally, the rich knowledge structure of KGs could be leveraged to enhance the retrieval of relevant document snippets that describe the indicators within these standards. Accordingly, we propose a framework aimed at accessing the capabilities of KGs and LLMs in mapping indicators within sustainability reporting standards. This paper presents our framework, details two exploratory experiments, and discuses the preliminary results.
Keywords: peng; diprolea

CyberDS: Auditable Monitoring in the Cloud - Sorokin, Lev; Schöpp, Ulrich (Proceedings of the 43rd International Conference on Computer Safety, Reliability and Security (SAFECOMP), 2024)
When deploying safety-critical systems in the cloud, where deviations may have severe consequences, the assurance of critical decisions becomes essential. Typical cloud systems are operated by third parties and are built on complex software stacks consisting of e.g., Kubernetes, Istio, or Kafka, which due to their size are difficult to be verified. Nevertheless, one needs to make sure that safety-critical choices are made correctly. In this paper, we propose CyberDS, a flexible runtime monitoring approach designed to transparently monitor safety and data-related properties in the Cloud. CyberDS is based on combining distributed Datalog-based programs with tamper-proof storage based on Trillian to verify the premises of critical actions. We demonstrate our monitoring approach on an industrial use case that uses a cloud infrastructure for the orchestration of unmanned air vehicles.

Data-driven Earth-fault Localization via Neural Network Models for Classification and Regression - Wörmann, Julian; Amaya, Lana; Eichelseder, Max; Duchon, Markus; Schwefel, Hans-Peter (2024 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 2024)
Rapid detection and precise localization of earth faults are crucial for ensuring the safe operation of energy distribution grids. Although detection is generally accomplished using integrated power quality hardware, pinpointing the fault location frequently requires labor-intensive manual switching operations, which can lead to supply disruptions. This work explores two methods of automated, robust, and accurate earth fault localization in medium voltage power grids using machine learning techniques. A digital twin is utilized to simulate fault events thus enabling the generation of extensive data to facilitate the training of data-driven models for fault localization. We explore two shallow neural network models to tackle the fault localization problem. On the one hand, we predict potential fault locations via node classification. On the other hand, a regression model is realized, that maps input measurements to associated impedances.This research work considers real-world medium voltage grids and focuses on investigating the influence on accuracy, robustness and flexibility of the implemented fault localization methods. The aim is to characterize the application areas of each approach for distribution grid operators. Finally, we open a discussion on requirements and desirable improvements for future developments.

Flexible Modeling and Execution of Semantic Manufacturing Processes for Robot Systems - Kessler, Ingmar; Perzylo, Alexander (IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2024)
The potential benefits of digital transformation for manufacturing companies include reduced costs, increased interconnectedness, and improved adaptability. Semantic Web technologies such as IRIs, RDF graphs, OWL ontologies, and SPARQL requests are a well-known and actively researched approach for supporting these transformation efforts. One challenge with this concept of knowledge augmentation is identifying where and how to integrate such semantic technologies into a manufacturing system, as it could require frequent translations into other non-semantic representations, which may entail a loss of expressivity and other disadvantages. Therefore, this work aims to use semantic technologies in a knowledge-augmented robotic manufacturing platform as directly and natively as possible. This approach includes the semantic modeling of manufacturing processes (similarly to flow charts) and context knowledge such as generalized mechanisms of how to apply them. All of this semantic knowledge is instantiated and persistently stored in a Robot Knowledge Base application, which implements mechanisms to automatically derive the next robot skill invocations and their parameter values during process execution. These semantic description models and the Robot Knowledge Base were tested in simulation as well as integrated into a physical mobile robot system with an articulated arm tackling an industrial use case.
Keywords: peng; vojext

Mapping Solar Potential: Comprehensive PV System Detection with Aerial and Satellite Images Using SolarSAM - Matar, Jessy; Albrecht, Michael; Kandelaki, Aleksandre; Kotthoff, Florian; Duchon, Markus (IEEE SmartGridComm 2024 Conference, 2024)
Automatic recognition of photovoltaic (PV) systems through remote sensing is critical for energy and infrastructure planning. This study explores the efficacy of deep learning in detecting PV systems using remote sensing. We introduce the adaptation of the Segment Anything Model (SAM) to this task, marking the first application of SAM for the detection and delineation of PV installations. We train the model on high-resolution images and then test its performance on both high-resolution aerial images and lower-resolution satellite images. Our results demonstrate high detection accuracy and precision especially for aerial imagery. We underscore the potential of advanced deep learning techniques in detecting and monitoring PV installations, facilitating more effective planning and deployment of renewable energy resources. Our findings suggest that the application of SAM in this domain could lead to significant advancements in energy infrastructure development, offering a promising tool for optimizing the integration and management of solar energy systems on a global scale.
Keywords: Photovoltaic; Deep Learning; Remote Sensing; Solar Energy; asci; need.

Mitigating Conflicts in Architectural Design Decisions for Evolving Manufacturing Systems: Evaluation and Recommendations for Practitioners - Terzimehić, Tarik; Zoitl, Alois (29th International Conference on Emerging Technologies and Factory Automation (ETFA), 2024)
The design process for manufacturing systems involves different architectural design decisions (ADDs), including deploying software to hardware components, determining shopfloor topology, and planning production tasks for stations. These ADDs exhibit complex interactions, where the synthesis of one ADD impacts other ADDs, highlighting the need to investigate their dynamic interplay. However, current research prioritizes individual ADDs over exploring multiple ADDs together. This work investigates a multi-dimensional design space exploration (DSE) of deployment, shop-floor topology, and production planning ADDs. We introduce mitigation constraints to ensure the compatibility among these ADDs. To evaluate our approach, we apply it to a flexible manufacturing line demonstrator. Furthermore, we assess and compare various DSE workflows, providing preliminary guidance to software architects on mitigating conflicts and enhancing the quality of ADDs.
Keywords: Industry 4.0; IEC 61499; deployment synthesis; optimization; SMT; Design-Space Exploration; DSE; Model-based Systems engineering; MbSE

Modular Consistency Checking Between Heterogeneous Models Without Direct Data Exchange Between Collaborators - Bergemann, Sebastian; Benkendorf, Nina (ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems (MODELS Companion ’24), 2024)
When using model-based systems engineering to develop a system with different models, inconsistencies can occur between these models based on their information overlaps. Multiple approaches exist how to handle such multi-model inconsistencies, but all of them rely in some form on having complete access to all models or exchanging model data directly between all models. This is critical regarding confidentiality, especially when different parties, e.g., different companies, are involved in the model-based development and do not want to share all their model data with each other. Therefore, we present a concept on how to check consistency between models from different parties without directly exchanging model data between them. The concept comprises a central neutral instance that handles the consistency checks, adapters for the involved modeling tools that can trigger checks, and minimal data exchange between the central instance and the adapters based on viewtypes. The feasibility of this concept is demonstrated with a prototype implementation and evaluated on a real model environment with heterogeneous models. The concept and prototype are modular and extensible, and thus can be used to test further confidentiality preservation techniques.
Keywords: consistency management; multi-model inconsistency detection; collaborative model-based systems engineering; Model-based Systems Engineering; MBSE; AutoFOCUS3

Smart Energy Forecasting: An In-depth Study on Forecasting Methods for Electric-thermal Storage Systems - Sebastian Cubides-Herrera, Cristian; Mayer, Jan; Matar, Jessy; Duchon, Markus (IEEE SmartGridComm 2024 Conference, 2024)
Smart grid developments have gained significant attention due to their potential to optimise energy consumption and reduce environmental impacts. For this reason, it is crucial to forecast future state conditions such as power, temperatures, heat, or SOC (state-of-charge) to make the most accurate and suitable control decision depending on the context and need. Since many processes are hard to model, the forecasting task can be executed by exploiting the advantages of machine learning models such as LSTM, transformer, Autoformer, or CNN. Comparing the results to previous works, we can state that our best model also outperforms state-of-the-art and state-research forecasting methods for continuous variables like temperatures. The model’s ability to accurately predict future states allows for more informed and adaptive control decisions, leading to enhanced energy efficiency, reduced environmental impact, and improved grid stability.
Keywords: Forecasting; Time Series; Machine Learning; LSTM; Smart Grids; Univariate missing data; asci; resonance.

Towards Assuring EU AI Act Compliance and Adversarial Robustness of LLMs - Bueno Momčilović, Tomas; Buesser, Beat; Zizzo, Giulio; Purcell, Mark; Balta, Dian (AI Act Workshop, 19th International Conference on Wirtschaftsinformatik, September 2024, Würzburg, Germany, 2024)
Large language models are prone to misuse and vulnerable to security threats, raising significant safety and security concerns. The European Union's Artificial Intelligence Act seeks to enforce AI robustness in certain contexts, but faces implementation challenges due to the lack of standards, complexity of LLMs and emerging security vulnerabilities. Our research introduces a framework using ontologies, assurance cases, and factsheets to support engineers and stakeholders in understanding and documenting AI system compliance and security regarding adversarial robustness. This approach aims to ensure that LLMs adhere to regulatory standards and are equipped to counter potential threats.
Accepted for publication (exp. September 2024)
Keywords: peng

Towards Digital Sustainability Reporting: An Ontology for Mapping of Indicators in GRI and ESRS - Zhou, Yuchen; Cao, Yuan; Perzylo, Alexander (International Conference on Semantic Systems (SEMANTiCS), 2024)
Sustainability reporting by Small and Medium Enterprises (SMEs) is gaining importance. SMEs form the backbone of European industries, and their customers rely on them to ensure regulatory compliance. In preparing sustainability reports, a combination of standards is commonly used, which encompasses overlapping yet distinct requirements on sustainability indicators. Different standards categorize shared indicators under varying topics, while they also mandate unique indicators to assess identical sustainability phenomena. This poses challenges for SMEs in reporting against multiple standards. Considerable human efforts are demanded to determine the interconnected requirements across different standards. Additionally, reporting on overlapping indicators for new standards results in significant redundant work. Mapping of indicators between different standards allows the semantic interoperability of standards by indicating matching and distinct requirements, aiding in addressing these challenges. Therefore, this paper focuses on developing an ontology for mapping indicators from two significant standards, GRI and ESRS. We introduce the Sustainability Reporting Standards Ontology (RSO). RSO formally represents environmental indicators in GRI and ESRS, and is available online. Furthermore, we provide an ontology-based mapping between indicators, supported by concrete examples that illustrate the interconnections between them.
Keywords: peng; diprolea

Towards the Estimation of Quality Attributes on System Model Histories - Blaschke, Konstantin Rupert; Barner, Simon (ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems (MODELS Companion '24), 2024)
Companies increasingly rely on Model-Based Systems Engineering to develop Cyber-Physical Systems such as cars, aircraft, or medical devices. The quality of engineering model artifacts is key to efficient collaboration in systems engineering with multi-tier supply chains. Ensuring model artifact quality and comprehensibility for practitioners is challenging. Manual reviews are time- and cost-intensive and subject to bias, whereas existing automated methods based on syntactical rules and model metrics are limited in scope. The paper presents work towards swift quality feedback to system engineers during modeling. The concept allows domain and project-specific context and is applicable to industry-size model artifacts. We implement a data-driven estimation that combines automated model metric extraction with expert quality assessments. We leverage the system model version history from an open-source miniature automotive demonstrator. We assess the model versions’ comprehensibility and showcase a semi-automated pipeline to initiate a model quality estimator. We achieve an average accuracy of 0.94 with a random forest approach on our test data.
Keywords: Model-based Systems Engineering; MBSE; Model Quality; Model Metrics; Quality Assessment; Model Review; AutoFOCUS3

You Can Only Verify When You Know the Answer: Feature-Based Explanations Reduce Overreliance on AI for Easy Decisions, but Not for Hard Ones - Zhang, Zelun Tony; Buchner, Felicitas; Liu, Yuanting; Butz, Andreas (Mensch und Computer 2024 (MuC ’24), 2024)
Explaining the mechanisms behind model predictions is a common strategy in AI-assisted decision-making to help users rely appropriately on AI. However, recent research shows that the effectiveness of explanations depends on numerous factors, leading to mixed results, with many studies finding no effect or even an increase in overreliance, while explanations do improve appropriate reliance in other studies. We consider the factor of decision difficulty to better understand when feature-based explanations can mitigate overre-liance. To this end, we conducted an online experiment (N = 200) with carefully selected task instances that cover a wide range of difficulties. We found that explanations reduce overreliance for easy decisions, but that this effect vanishes with increasing decision difficulty. For the most difficult decisions, explanations might even increase overreliance. Our results imply that explanations of the model's inner workings are only helpful for a limited set of decision tasks where users easily know the answer themselves.
Keywords: explainable AI; overreliance; human-AI decision-making; AI-assisted decision-making; decision difficulty; online experiment

A cost-efficient FOC-controlled haptic knob for industrial robot programming with force feedback - Ding, Junsheng; Fu, Xiangyu; Wei, Tiantian; Perzylo, Alexander (IEEE International Conference on Industrial Informatics (INDIN), 2024)
Robot programming is still an elaborate process in industrial assembly, especially in cases requiring a specific force profile for successfully executing an assembly step. In this work, we present the Haptic Knob, a low-cost haptic device (around 100 EUR on hardware costs) for (currently) one-dimensional robot movement programming on both position and force profiles. The Haptic Knob is equipped with a Field-Oriented Controlled (FOC) motor and a positional encoder that reads the human input position and generates feedback forces to the operator. The Haptic Knob is not equipped with an external Force/Torque sensor to measure the human input force, but works together with the impedance controller of the robot arm, which allows a dynamic force teach-in. A control interface is implemented to map the positional signal from the Haptic Knob to the movement of different actuators, as well as the force/torque signals from various devices to the Haptic Knob for force feedback. We showcase and validate the proposed hardware and control interface on the industrial use case of automotive fuse box assembly.
Keywords: peng

Generic Analysis of Model Product Lines via Constraint Lifting - Bayha, Andreas; Aravantinos, Vincent (arXiv:2008.11427 [cs.SE], 2024)
Engineering a product-line is more than just describing a product-line: to be correct, every variant that can be generated must satisfy some constraints. To ensure that all such variants will be correct (e.g. well-typed) there are only two ways: either to check the variants of interest individually or to come up with a complex product-line analysis algorithm, specific to every constraint. In this paper, we address a generalization of this problem: we propose a mechanism that allows to check whether a constraint holds simultaneously for all variants which might be generated. The main contribution of this paper is a function that assumes constraints that shall be fulfilled by all variants and generates ("lifts") out of them constraints for the product-line. These lifted constraints can then be checked directly on a model product-line, thus simultaneously be verified for all variants. The lifting is formulated in a very general manner, which allows to make use of generic algorithms like SMT solving or theorem proving in a modular way. We show how to verify lifted constraints using SMT solving by automatically translating model product-lines and constraints. The applicability of the approach is demonstrated with an industrial case study, in which we apply our lifting to a domain specific modelling language for manufacturing planning. Finally, a runtime analysis shows scalability by analyzing different model product-lines with production planning data from the BMW Group and Miele.
Keywords: Model-based Systems Engineering; MbSE; Product Line Analysis; Variability; Product Lines; Domain Specific Languages

Intuitive Instruction of Robot Systems: Semantic Integration of Standardized Skill Interfaces - Ding, Junsheng; Kessler, Ingmar; Perzylo, Alexander; Knauer, Markus; Dömel, Andreas; Willibald, Christoph; Riedel, Sebastian; Profanter, Stefan; Brunner, Sebastian; Dunaev, Arsenii; Li, Le; Brucker, Manuel (IEEE International Conference on Industrial Informatics (INDIN), 2024)
This work aims at facilitating the integration of industrial robots and other devices such as their gripper tools at small and medium-sized enterprises (SMEs). For this purpose, an intuitive user interface for the skill-based instruction of robot systems is combined with standardized OPC UA-based skill interfaces that support various hardware and software resources from different manufacturers. Special emphasis is laid on supporting different user groups with varying levels of expertise. Production system engineers are provided with a detailed graphical user interface (GUI) for hierarchically defining new skills by combining preexisting ones. System operators receive a simplified view with limited complexity for process instruction and changing high-level task parameterizations. The skills and relevant semantic context knowledge about products, processes, and resources (PPR) are formally represented in OWL ontologies to enable hardware-agnostic process descriptions that can be deployed to different production environments, while automatically deriving parameterizations for skill invocations. The proposed concept has been qualitatively evaluated in two real-world robot workcells based on a smartphone accessory packaging use case.
Keywords: peng; iriss

Latest Advances on Deterministic on Deterministic Wired/Wireless Industrial Networks - C. Sofia, Rute (6G Connectivity-Systems, Technologies, and applications, 2024)
This chapter investigates the most recent advances in deterministic OFDMA- based Wi-Fi for industrial applications. It concentrates on techniques that provide deterministic support in industrial networks where Wi-Fi 6 is used as an additional technology to a deterministic wired core. It examines scenarios and traffic patterns that are expected to be supported and proposes enhance- ments to further support deterministic guarantees in a way that is compatible with the existing deterministic support in the wired core. Furthermore, the chapter introduces the network simulator v3 DetNetWiFi framework, which was developed based on a realistic deterministic wired/wireless demonstra- tor, and which can be used to validate and experiment on a wired/wireless infrastructure that incorporates novel mechanisms for time synchronization, time-aware scheduling, and multi-AP co-OFDMA resource coordination.
Keywords: IIoT; wireless; TSN; Wi-Fi6

Multidimensional Design Space Exploration for Evolving Manufacturing Systems: Interplay of Deployment, Topology, and Production Planning Architectural Decisions - Terzimehić, Tarik; Erler, Daniel; Zoitl, Alois (IEEE Transactions on Industrial Informatics, 2024)
Optimizing evolving manufacturing systems often requires considering the interplay of various architectural design decisions (ADDs), especially at the lower levels of the automation pyramid. Here, decisions about programmable logic controllers, stations, and shop-floor topology directly impact product characteristics. This necessitates a multidimensional design space exploration (DSE) approach that synthesizes diverse ADDs together. However, integrating diverse ADDs within the complex context of industrial automation remains a challenge, and existing DSE solutions from other domains often lack direct applicability. This work introduces a novel multidimensional DSE approach for such systems, leveraging domain-specific input models to automatically generate valid and optimal deployment, shop-floor topology, and production planning ADDs. We evaluate the approach by assessing the quality of derived ADDs against various domain optimization goals. This evaluation compares our approach to both random and manual ADDs, utilizing a prototype implementation and realistic manufacturing architectures.
Keywords: Industry 4.0; IEC 61499; deployment synthesis; optimization; SMT; Design-Space Exploration; DSE; Model-based Systems engineering; MbSE

Hierarchisch verteilte Verwaltungsschalen-Registries – Nutzung der Verwaltungsschale in verteilten Umgebungen - Kuhn, Thomas; Schnicke, Frank; Porta, Daniel; Klausmann, Tobias; Tröger, Karl; Müller, Thomas; Heppner, Sebastian; Terzimehić, Tarik (Automation 2024, 2024)

On Safety Assurance of Symbolic Artificial Intelligence - Guzman Cancimance, Michell; Bouchekir, Radouane; Alasdair, Cook; Haindl, Johannes (International Conference on Software Quality, Reliability, and Security Companion, 2024)

Requirements for reliable wireless industrial applications (draft-ietf-detnet-raw-industrial-req-01) - C. Sofia, Rute; M. Mendes, Paulo; Schooler, Eve; Bernardos, Carlos (IETF working group DetNet, 2024)
This document provides an overview of the communication requirements for handling reliable wireless services in the context of industrial environments. The aim of the draft is to raise awareness of the communication requirements of current and future wireless industrial services; how they can coexist with wired infrastructures; the key drivers for reliable wireless integration; the relevant communication requirements to be considered; the current and future challenges arising from the use of wireless services; and the potential benefits of wireless communication.
Keywords: IIoT

Structured Product Engineering: Ontology-based Advanced Product Quality Planning - Srinivasan, Uppili; Mittel, Dominik; Lemberger, Daniel; Perzylo, Alexander (CIRP Conference on Intelligent Computation in Manufacturing Engineering (ICME), 2024)
Increasingly complex products make it difficult to efficiently meet customer requirements and underline the need for structured product engineering. Ontologies enable the combination of heterogeneous data sources and linking of existing knowledge to automatically derive further insights. In this work, an ontology-based approach for a structured product engineering process is introduced. We develop an ontology for Advanced Product Quality Planning (APQP)-guided product development and combine it with semantic models regarding production knowledge, ontology-based geometric representations, and a graphical user interface. As a result, an assistance system that provides support from customer inquiry to product design is developed.
Keywords: peng; diprolea

Towards Assurance of LLM Adversarial Robustness using Ontology-Driven Argumentation - Bueno Momčilović, Tomas; Buesser, Beat; Zizzo, Giulio; Purcell, Mark; Balta, Dian (xAI 2024: World Conference on eXplainable Artificial Intelligence, 2024)
Despite the impressive adaptability of large language models (LLMs), challenges remain in ensuring their security, transparency, and interpretability. Given their susceptibility to adversarial attacks, LLMs need to be defended with an evolving combination of adversarial training and guardrails. However, managing the implicit and heterogeneous knowledge for continuously assuring robustness is difficult. We introduce a novel approach for assurance of the adversarial robustness of LLMs based on formal argumentation. Using ontologies for formalization, we structure state-of-the-art attacks and defenses, facilitating the creation of a human-readable assurance case, and a machine-readable representation. We demonstrate its application with examples in English language and code translation tasks, and provide implications for theory and practice, by targeting engineers, data scientists, users, and auditors.
Keywords: peng

Analyzing the Accessibility of GitHub Repositories for PyPI and NPM Libraries - Tsakpinis, Alexandros; Pretschner, Alexander (EASE '24: Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering, 2024)
Industrial applications heavily rely on open-source software (OSS) libraries, which provide various benefits. But, they can also present a substantial risk if a vulnerability or attack arises and the community fails to promptly address the issue and release a fix due to inactivity. To be able to monitor the activities of such communities, a comprehensive list of repositories for the libraries of an ecosystem must be accessible. Based on these repositories, integrated libraries of an application can be monitored to observe whether they are adequately maintained. In this descriptive study, we analyze the accessibility of GitHub repositories for PyPI and NPM libraries. For all available libraries, we extract assigned repository URLs, direct dependencies and use the page rank algorithm to comprehensively analyze the ecosystems from a library and dependency chain perspective. For invalid repository URLs, we derive potential reasons. Both ecosystems show varying accessibility to GitHub repository URLs, depending on the page rank score of the analyzed libraries. For individual libraries, up to 73.8% of PyPI and up to 69.4% of NPM libraries have repository URLs. Within dependency chains, up to 80.1% of PyPI libraries have URLs, while up to 81.1% for NPM. That means, most libraries, especially the ones of increasing importance, can be monitored on GitHub. Among the most common reasons for invalid repository URLs is no URLs being assigned at all, which amounts up to 17.9% for PyPI and up to 39.6% for NPM. Package maintainers should address this issue and update the repository information to enable monitoring of their libraries.

Emergent Needs in Assuring Security-Relevant Compliance of Information Systems - Bueno Momčilović, Tomas; Balta, Dian (EICC 2024: European Interdisciplinary Cybersecurity Conference, 2024)
Establishing and assuring compliance of information systems is a difficult task with potentially critical impact to the security of those same systems. Ambiguously worded laws such as the Digital Operational Resilience Act make it difficult for organizations to determine which actions to undertake in pursuit of compliance. This ambiguity prompts auditors, compliance officers and other involved roles to interpret the meaning and implement measures according to best judgment, resulting in an intricate back-and-forth process with remaining uncertainties. In a qualitative case study involving a multinational financial corporation and its information systems, we explore the needs of stakeholders that emerge from the interpretations and uncertainties in the process. We model the complex interconnections in a figure from a deeper look in the subcase on establishing and assuring compliance of identity and access management (IAM) procedures. Finally, we discuss potential avenues for resolving these problems.
Keywords: peng; Assurance; Identity and Access Management; Knowledge Modeling; Regulatory Compliance

A Framework for Cognitive, Decentralized Container Orchestration - C. Sofia, Rute (IEEE Access, 2024)
Keywords: IIoT

A Model-Based Systems Engineering Plugin for Cloud Security Architecture Design - Dantas, Yuri Gil; Nigam, Vivek; Schöpp, Ulrich (SN Computer Science, 2024)
Security is one of the biggest concerns for cloud infrastructures. Cloud infrastructures are susceptible to a wide range of threats, including external and internal threats. Without proper security mechanisms, these threats may compromise the security properties of services hosted in the cloud. To secure cloud infrastructures against threats, it is crucial to perform a threat analysis in the early stages of the system development (i.e., during the design of the system architecture). Threat Analysis and Risk Assessment (TARA) is a well-known approach used by researchers and practitioners. TARA consists of several activities, including asset identification, threat scenarios, attack paths, and risk treatment decision. The risk treatment decision activity involves selecting appropriate security measures to mitigate the identified threat scenarios. In the current state of practice, TARA activities are performed manually by engineers, leading to time-consuming processes and potential errors. In our previous article, we proposed a logic programming tool to enable the automation of TARA activities, including the recommendation of cloud-based security measures. This article proposes Security Pattern Synthesis, a Model-Based Systems Engineering (MBSE) plugin for securing cloud architectures. Security Pattern Synthesis is implemented in Java while using the previously proposed logic-programming tool as a backend to reason about the security of the cloud architecture.
Keywords: Securing cloud architectures; Model-based Systems Engineering; MbSE; Security architecture patterns; Automation; AutoFOCUS3

Assessing Quality Metrics for Neural Reality Gap Input Mitigation in Autonomous Driving Testing - Lambertenghi, Stefano Carlo; Stocco, Andrea (Proceedings of 17th IEEE International Conference on Software Testing, Verification and Validation, 2024)
Simulation-based testing of automated driving systems (ADS) is the industry standard, being a controlled, safe, and cost-effective alternative to real-world testing. Despite these advantages, virtual simulations often fail to accurately replicate real-world conditions like image fidelity, texture representation, and environmental accuracy. This can lead to significant differences in ADS behavior between simulated and real-world domains, a phenomenon known as the sim2real gap. Researchers have used Image-to-Image (I2I) neural translation to mitigate the sim2real gap, enhancing the realism of simulated environments by transforming synthetic data into more authentic representations of real-world conditions. However, while promising, these techniques may potentially introduce artifacts, distortions, or inconsistencies in the generated data that can affect the effectiveness of ADS testing. In our empirical study, we investigated how the quality of image-to-image (I2I) techniques influences the mitigation of the sim2real gap, using a set of established metrics from the literature. We evaluated two popular generative I2I architectures, pix2pix and CycleGAN, across two ADS perception tasks at a model level, namely vehicle detection and end-to-end lane keeping, using paired simulated and real-world datasets. Our findings reveal that the effectiveness of I2I architectures varies across different ADS tasks, and existing evaluation metrics do not consistently align with the ADS behavior. Thus, we conducted task-specific fine-tuning of perception metrics, which yielded a stronger correlation. Our findings indicate that a perception metric that incorporates semantic elements, tailored to each task, can facilitate selecting the most appropriate I2I technique for a reliable assessment of the sim2real gap mitigation.

Predicting Safety Misbehaviours in Autonomous Driving Systems using Uncertainty Quantification - Ruben, Grewal; Paolo, Tonella; Stocco, Andrea (Proceedings of 17th IEEE International Conference on Software Testing, Verification and Validation, 2024)
The automated real-time recognition of unexpected situations plays a crucial role in the safety of autonomous vehicles, especially in unsupported and unpredictable scenarios. This paper evaluates different Bayesian uncertainty quantification methods from the deep learning domain for the anticipatory testing of safety-critical misbehaviours during system-level simulation-based testing. Specifically, we compute uncertainty scores as the vehicle executes, following the intuition that high uncertainty scores are indicative of unsupported runtime conditions that can be used to distinguish safe from failure-inducing driving behaviors. In our study, we conducted an evaluation of the effectiveness and computational overhead associated with two Bayesian uncertainty quantification methods, namely MCDropout and Deep Ensembles, for misbehaviour avoidance. Overall, for three benchmarks from the Udacity simulator comprising both out-of-distribution and unsafe conditions introduced via mutation testing, both methods successfully detected a high number of out-of-bounds episodes providing early warnings several seconds in advance, outperforming two state-of-the-art misbehaviour prediction methods based on autoencoders and attention maps in terms of effectiveness and efficiency. Notably, Deep Ensembles detected most misbehaviours without any false alarms and did so even when employing a relatively small number of models, making them computationally feasible for real-time detection. Our findings suggest that incorporating uncertainty quantification methods is a viable approach for building fail-safe mechanisms in deep neural network-based autonomous vehicles.

Towards Ontology-based Handling of Uncertainties in Robotic Assembly - Mittel, Dominik; Perzylo, Alexander; Kessler, Ingmar; Ding, Junsheng (IEEE International Conference on Robotics and Automation (ICRA), Workshop on Ontologies and Standards for Robotics and Automation (WOSRA), 2024)
In the context of Industry 4.0, robot systems need to handle new and more complex tasks to produce highly customized products at small lot sizes. Small and medium-sized enterprises, in particular, lack the expert knowledge to parameterize such systems and to take relevant uncertainties into account. Ontologies provide functionality to explicitly encode knowledge using a common vocabulary. In this work, ideas towards the ontology-based representation of uncertainties and associated handling strategies are presented. We define an uncertainty taxonomy and combine it with knowledge about products, manufacturing processes, and resources, following the PPR modeling paradigm. The concept is implemented and tested using a robotic assembly task of an electronic component. As a result, the integration of different types of knowledge enables the automatic adjustment of robot processes based on the consideration of involved uncertainties. This may lead to an easier adaption of robot programs for new products and a more robust operation.
Keywords: peng; vojext

Where Do You Look When Unlocking Your Phone? A Field Study of Gaze Behaviour During Smartphone Unlock - Abdrabou, Yasmeen; Omelina, Tatiana; Dietz, Felix; Khamis, Mohamed; Alt, Florian; Hassib, Mariam (CHI Conference on Human Factors in Computing Systems., 2024)
Eye gaze has emerged as a promising avenue for implicit authentication/ identification on smartphones, offering the potential for seamless user identification and two-factor authentication. However, a crucial gap exists in understanding eye gaze behaviour specifically during smartphone unlocks. This lack of understanding is magnified by scenarios where users’ faces are not fully visible in front cameras, leading to inaccurate gaze estimation. In this work, we conducted a 24-hour in-the-wild study tracking 21 users’ eye gaze during smartphone unlocks. Our findings highlight substantial eye gaze behaviour variations influenced by authentication methods, physical activity, and environment. Our findings provide insights to enhance and adapt implicit user identification/authentication systems based on gaze tracking on smartphones taking into consideration different users’ behaviour, and environmental effects.
Keywords: Gaze Behaviour; Authentication; Identification; Smartphones; Eye Tracking

OpenSBT: A Modular Framework for Search-based Testing of Automated Driving Systems - Sorokin, Lev; Munaro, Tiziano; Damir, Safin; Liao, Brian Hsuan-Cheng; Molin, Adam (arXiv:2306.10296 [cs.SE], 2024)
Search-based software testing (SBT) is an effective and efficient approach for testing automated driving systems (ADS). However, testing pipelines for ADS testing are particularly challenging as they involve integrating complex driving simulation platforms and establishing communication protocols and APIs with the desired search algorithm. This complexity prevents a wide adoption of SBT and thorough empirical comparative experiments with different simulators and search approaches. We present OpenSBT, an open-source, modular and extensible framework to facilitate the SBT of ADS. With OpenSBT, it is possible to integrate simulators with an embedded system under test, search algorithms and fitness functions for testing. We describe the architecture and show the usage of our framework by applying different search algorithms for testing Automated Emergency Braking Systems in CARLA as well in the high-fidelity Prescan simulator in collaboration with our industrial partner DENSO. OpenSBT is available at this https URL.
Accepted for the Demonstration Track at 46th International Conference on Software Engineering (ICSE 2024) | April 14-20.
Keywords: Search-based software testing; metaheuristics; scenario-based testing; autonomous driving; automated driving; MbSE; Model-based Systems Engineering

Automated Model Quality Estimation and Change Impact Analysis on Model Histories - Blaschke, Konstantin Rupert (IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion ’24), 2024)
The development of Cyber-Physical Systems combines hardware with software in complex applications. To mitigate the complexity, collaborating system engineers rely on model-based approaches in systems engineering. Updates and function enhancements lead to frequent changing constraints and objectives. These changes increase the need to rework and extend model artifacts of the system. This can cause quality degradation over time due to errors, knowledge disparities or a lack of guidelines. To enable efficient collaboration and reduce maintenance costs in model-based systems engineering, industry needs a cost-efficient, scalable approach to monitor, and control model quality. The work outlines a doctoral thesis investigating the potential of automated data-driven quality assessment strategies using model artifact history and model changes. We will extract metrics and model changes to establish a quality feedback loop for system engineers. We aim to leverage the results of manual model quality assessments to incorporate domain-specific expert knowledge into the automated strategy. The main goal is to lower the effort of model quality assessments and provide practitioners with foresight on quality development and estimate task effort to improve model artifact quality.
Keywords: Model-based Systems Engineering; Model Quality; Model Metrics; Quality Assessment; Model Review; Change-Impact Analysis; MbSE; AutoFOCUS3

Challenges of Assuring Compliance of Information Systems in Finance - Bueno Momčilović, Tomas; Balta, Dian (Software Quality as a Foundation for Security. SWQD 2024, 2024)
Assuring regulatory compliance of information systems (IS), as a bundle of software systems and business processes, is an important, but costly and continuous effort. Laws formulate demands for quality properties in ambiguous language, requiring substantial interpretation. Industry standards provide support, but remain generic and applicable to heterogeneous company IS contexts. Before compliance measures can be implemented in software assets and processes, a specific interpretation based on the context of each company is a prerequisite. Compliance experts such as auditors support this process by accounting for the perspectives of company stakeholders. Ultimately, however, the complexity of the required knowledge, legal and technical facets prevents organizations from continuously establishing situational awareness or guarantees, and answering the question: is the company currently compliant? We illustrate the complexity of assuring compliance in a qualitative case study with a European, software-driven corporation in the financial industry. Through modeling of an example of annual audits and analyzing literature, we describe the perspectives of the involved stakeholders with their roles, knowledge needs and facets. We observe six challenges: (1) large number of items and links; (2) unclear and implicit links; (3) siloing of knowledge; (4) multiple sources of truth; (5) high costs of learning from audits; and (6) uncertain results of traditional auditing. We discuss the implications of these observed challenges, and briefly explore potential avenues for resolution.
Keywords: peng

Guiding the Search Towards Failure-Inducing Test Inputs Using Support Vector Machines - Sorokin, Lev (Workshop Paper, 2024)
In this paper, we present NSGA-II-SVM (Non-dominated Sorting Genetic Algorithm with Support Vector Machine Guidance), a novel learnable evolutionary and search-based testing algorithm that leverages Support Vector Machine (SVM) classification models to direct the search towards failure-revealing test inputs. Supported by genetic search, NSGA-II-SVM creates iteratively SVM-based models of the test input space, learning which regions in the search space are promising to be explored. A subsequent sampling and repetition of evolutionary search iterations allow to refine and make the model more accurate in the prediction. Our preliminary evaluation of NSGA-II-SVM by testing an Automated Valet Parking system shows that NSGA-II-SVM is more effective in identifying more critical test cases than a state-of-the-art learnable evolutionary testing technique as well as naive random search.

Software in the Manufacturing Industry: Emerging Security Challenge Areas for IIoT Platforms - Landeck, Yannick; Balta, Dian; Wimmer, Martin; Knierim, Christian (46th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP ’24), 2024)
Industrial Internet of Things (IIoT) platforms connect services and computation resources to industrial devices. They increase flexibility, scalability, and provide a wider application portfolio for automated production. However, in a growing ecosystem of software suppliers, manufacturing companies are concerned whether the security of third-party software meets the requirements of the industry. In this paper, we analyse IIoT reference architecture components and corresponding stakeholders to draw implications on security. In particular, we identify four security challenge areas of IIoT platforms, show relations to existing research, and highlight directions for future work.
Keywords: peng; Software Security; Manufacturing; IIoT Platforms; Assurance

DiProLeA - Digitale Produktentstehungsprozesse: Durchgängige sozio-technische Assistenzsysteme für die Produktentstehung - Diekmann, Pia; Holl, Anika; Hubert, Andreas; Kröhn, Michael; Leipold, Eva; Lemberger, Daniel; Lenz, Claus; Mittel, Dominik; Perzylo, Alexander (wt Werkstattstechnik online, 2024)
In Zeiten von kürzeren Entwicklungszyklen und gleichzeitig einer höheren Komplexität von Produkten steigen die Anforderungen an die Digitalisierung von Prozessen. Unternehmen verwalten dabei produktspezifische Informationen des Produktentstehungsprozesses häufig aufgabenbezogen in heterogenen Systemen. Mit digitalen Werkzeugen sollen Arbeiter unterstützt und Prozesse sicherer gestaltet werden. Das Projekt DiProLeA erforscht ein ganzheitliches Assistenzsystem für den Produktentstehungsprozess vom Produktdesign bis zur Fertigung. Das Konzept wird anhand zweier Anwendungsfälle veranschaulicht. Das Projekt wurde in der Fördermaßnahme "Beherrschung der Komplexität soziotechnischer Systeme" eingereicht und bildet damit einen Beitrag zum Advanced Systems Engineering für die Wertschöpfung von morgen.
Keywords: peng; diprolea

Towards Continuous Assurance Case Creation for ADS with the Evidential Tool Bus - Sorokin, Lev; Bouchekir, Radouane; Beyene, Tewodros; Liao, Brian Hsuan-Cheng; Molin, Adam (European Dependable Computing Conference, 2024)

Dynamic Event-based Optical Identification and Communication - von Arnim, Axel; Lecomte, Jules; Wozniak, Stanislaw; Elosegui, Naima; Pantazi, Angeliki (Frontiers in Neurorobotics, 2024)
Optical identification is often done with spatial or temporal visual pattern recognition and localization. Temporal pattern recognition, depending on the technology, involves a trade-off between communication frequency, range and accurate tracking. We propose a solution with light-emitting beacons that improves this trade-off by exploiting fast event-based cameras and, for tracking, sparse neuromorphic optical flow computed with spiking neurons. In an asset monitoring use case, we demonstrate that the system, embedded in a simulated drone, is robust to relative movements and enables simultaneous communication with, and tracking of, multiple moving beacons. Finally, in a hardware lab prototype, we achieve state-of-the-art optical camera communication frequencies in the kHz magnitude.
Keywords: Neuromorphic Computing; Event-Based Sensing; Optical Camera Communication; Optical Flow

EyeSeeIdentity: Exploring Natural Gaze Behavior for Implicit User Identification during Photo Viewing - Abdrabou, Yasmeen; Hassib, Mariam; Hu, Shuqin; Pfeuffer, Ken; Khamis, Mohamed; Bulling, Andreas; Alt, Florian (Symposium on Usable Security and Privacy (USEC) 2024, 2024)
—Existing gaze-based methods for user identification either require special-purpose visual stimuli or artificial gaze behavior. Here, we explore how users can be differentiated by analyzing natural gaze behavior while freely looking at images. Our approach is based on the observation that looking at different images, for example, a picture from your last holiday, induces stronger emotional responses that are reflected in gaze behavior and, hence, are unique to the person having experienced that situation. We collected gaze data in a remote study (N = 39) where participants looked at three image categories: personal images, other people’s images, and random images from the Internet. We demonstrate the potential of identifying different people using machine learning with an accuracy of 85%. The results pave the way for a new class of authentication methods solely based on natural human gaze behavior.

A Handbook on Teaching Empirical Software Engineering - Mendez, Daniel; Avgeriou, Paris; Kalinowski, Marcos; bin Ali, Nauman (A Handbook on Teaching Empirical Software Engineering, 2024)
This handbook exploits the profound experience and expertise of well-established scholars in the empirical software engineering community to provide guidance and support in teaching various research methods and fundamental concepts. A particular focus is thus on combining research methods and their epistemological settings and terminology with didactics and pedagogy for the subject. The book covers the most essential contemporary research methods and philosophical and cross-cutting concerns in software engineering research, considering both academic and industrial settings, at the same time providing insights into the effective teaching of concepts and strategies. To this end, the book is organized into four major parts. In the first part, the editors set the foundation with two chapters; one laying out the larger context of the discipline for a positioning of the remainder of this book, and one guiding the creation of a syllabus for courses in empirical software engineering. The second part of the book lays the fundamentals for teaching empirical software engineering, addressing more cross-cutting aspects from theorizing and teaching research designs to measurement and quantitative data analysis. In the third part, general experiences and personal reflections from teaching empirical software engineering in different settings are shared. Finally, the fourth part contains a number of carefully selected research methods, presented through an educational lens. Next to the chapter contributions themselves that provide a more theoretical perspective and practical advice, readers will find additional material in the form of, for example, slide sets and tools, in an online material section. The book mainly targets three different audiences: (1) educators teaching empirical software engineering to undergraduate, postgraduate or doctoral students, (2) professional trainers teaching the basic concepts of empirical software engineering to software professionals, and (3) students and trainees attending such courses.

A Second Look at the Impact of Passive Voice Requirements on Domain Modeling: Bayesian Reanalysis of an Experiment - Frattini, Julian; Fucci, Davide; Mendez, Daniel; Torkar, Richard (1st International Workshop on Methodological Issues with Empirical Studies in Software Engineering, 2024)
The quality of requirements specifications may impact subsequent, dependent software engineering (SE) activities. However, empirical evidence of this impact remains scarce and too often superficial as studies abstract from the phenomena under investigation too much. Two of these abstractions are caused by the lack of frameworks for causal inference and frequentist methods which reduce complex data to binary results. In this study, we aim to demonstrate (1) the use of a causal framework and (2) contrast frequentist methods with more sophisticated Bayesian statistics for causal inference. To this end, we reanalyze the only known controlled experiment investigating the impact of passive voice on the subsequent activity of domain modeling. We follow a framework for statistical causal inference and employ Bayesian data analysis methods to re-investigate the hypotheses of the original study. Our results reveal that the effects observed by the original authors turned out to be much less significant than previously assumed. This study supports the recent call to action in SE research to adopt Bayesian data analysis, including causal frameworks and Bayesian statistics, for more sophisticated causal inference.

Adversarial Machine Learning in Industry: A Systematic Literature Review - Jedrzejewski, Felix Viktor; Thode, Lukas; Fischbach, Jannik; Gorschek, Tony; Mendez, Daniel; Lavesson, Niklas (Computers & Security, 2024)
Adversarial Machine Learning (AML) discusses the act of attacking and defending Machine Learning (ML) Models, an essential building block of Artificial Intelligence (AI). ML is applied in many software-intensive products and services and introduces new opportunities and security challenges. AI and ML will gain even more attention from the industry in the future, but threats caused by already-discovered attacks specifically targeting ML models are either overseen, ignored, or mishandled. Current AML research investigates attack and defense scenarios for ML in different industrial settings with a varying degree of maturity with regard to academic rigor and practical relevance. However, to the best of our knowledge, a synthesis of the state of academic rigor and practical relevance is missing. This literature study reviews studies in the area of AML in the context of industry, measuring and analyzing each study’s rigor and relevance scores. Overall, all studies scored a high rigor score and a low relevance score, indicating that the studies are thoroughly designed and documented but miss the opportunity to include touch points relatable for practitioners.
Keywords: Adversarial machine learning; Industry; Rigor; Relevance; State of evidence

Applying Bayesian Data Analysis for Causal Inference about Requirements Quality: a Controlled Experiment - Frattini, Julian; Fucci, Davide; Torkar, Richard; Montgomery, Lloyd; Unterkalmsteiner, Michael; Fischbach, Jannik; Mendez, Daniel (Empirical Software Engineering, 2024)
It is commonly accepted that the quality of requirements specifications impacts subsequent software engineering activities. However, we still lack empirical evidence to support organizations in deciding whether their requirements are good enough or impede subsequent activities. We aim to contribute empirical evidence to the effect that requirements quality defects have on a software engineering activity that depends on this requirement. We conduct a controlled experiment in which 25 participants from industry and university generate domain models from four natural language requirements containing different quality defects. We evaluate the resulting models using both frequentist and Bayesian data analysis. Contrary to our expectations, our results show that the use of passive voice only has a minor impact on the resulting domain models. The use of ambiguous pronouns, however, shows a strong effect on various properties of the resulting domain models. Most notably, ambiguous pronouns lead to incorrect associations in domain models. Despite being equally advised against by literature and frequentist methods, the Bayesian data analysis shows that the two investigated quality defects have vastly different impacts on software engineering activities and, hence, deserve different levels of attention. Our employed method can be further utilized by researchers to improve reliable, detailed empirical evidence on requirements quality.

Automated Security Findings Management: A Case Study in Industrial DevOps - Voggenreiter, Markus; Angermeir, Florian; Moyón, Fabiola; Schöpp, Ulrich; Bonvin, Pierre-Louis (46th International Conference on Software Engineering: Software Engineering in Practice, 2024)
In recent years, DevOps, the unification of development and operation workflows, has become a trend for the industrial software development lifecycle. Security activities turned into an essential field of application for DevOps principles as they are a fundamental part of secure software development in the industry. A common practice arising from this trend is the automation of security tests that analyze a software product from several perspectives. To effectively improve the security of the analyzed product, the identified security findings must be managed and looped back to the project team for stakeholders to take action. This management must cope with several challenges ranging from low data quality to a consistent prioritization of findings while following DevOps aims. To manage security findings with the same efficiency as other activities in DevOps projects, a methodology for the management of industrial security findings minding DevOps principles is essential. In this paper, we propose a methodology for the management of security findings in industrial DevOps projects, summarizing our research in this domain and presenting the resulting artifact. As an instance of the methodology, we developed the Security Flama, a semantic knowledge base for the automated management of security findings. To analyze the impact of our methodology on industrial practice, we performed a case study on two DevOps projects of a multinational industrial enterprise. The results emphasize the importance of using such an automated methodology in industrial DevOps projects, confirm our approach’s usefulness and positive impact on the studied projects, and identify the communication strategy as a crucial factor for usability in practice.

Automatic ESG Assessment of Companies by Mining and Evaluating Media Coverage Data: NLP Approach and Tool - Fischbach, Jannik; Mendez, Daniel; Frattini, Julian; Kosenkov, Oleksandr; Elahidoost, Parisa; Adam, Max; Dzhagatspanyan, Victor (5th Financial Narrative Processing Workshop, 2024)
Sustainable corporate behavior is increasingly valued by society and impacts corporate reputation and customer trust. Hence, companies regularly publish sustainability reports to shed light on their impact on environmental, social, and governance (ESG) factors. Problem: Sustainability reports are written by companies themselves and are therefore considered a company-controlled source. Contrary, studies reveal that non-corporate channels (e.g., media coverage) represent the main driver for ESG transparency. However, analysing media coverage regarding ESG factors is challenging since (1) the amount of published news articles grows daily, (2) media coverage data does not necessarily deal with an ESG-relevant topic, meaning that it must be carefully filtered, and (3) the majority of media coverage data is unstructured. Research Goal: We aim to extract ESG-relevant information from textual media reactions automatically to calculate an ESG score for a given company. Our goal is to reduce the cost of ESG data collection and make ESG information available to the general public. Contribution: Our contributions are three-fold: First, we publish a corpus of 432,411 news headlines annotated as being environmental-, governance-, social-related, or ESG-irrelevant. Second, we present our tool-supported approach called ESG-Miner capable of analyzing and evaluating headlines on corporate ESG-performance automatically. Third, we demonstrate the feasibility of our approach in an experiment and apply the ESG-Miner on 3000 manually labeled headlines. Our approach processes 96.7 % of the headlines correctly and shows a great performance in detecting environmental-related headlines along with their correct sentiment. We encourage fellow researchers and practitioners to use the ESG-Miner at this https URL.

Connecting the Dots of Knowledge in Agile Software Development - Ouriques, Raquel; Gorschek, Tony; Fagerholm, Fabian; Mendez, Daniel (XP Conference, 2024)
This article discusses the importance of managing knowledge as a resource due to its great potential to create economic value. We detail the types of knowledge resources, the challenges associated with their management, and potential solutions to maximise their utility. Our contribution is based on empirical studies performed in an industry context.

Designing a Syllabus for a Course on Empirical Software Engineering - Avgeriou, Paris; bin Ali, Nauman; Kalinowski, Marcos; Mendez, Daniel (Handbook on Teaching Empirical Software Engineering, 2024)
Increasingly, courses on Empirical Software Engineering research methods are being offered in higher education institutes across the world, mostly at the this http URL. and Ph.D. levels. While the need for such courses is evident and in line with modern software engineering curricula, educators designing and implementing such courses have so far been reinventing the wheel; every course is designed from scratch with little to no reuse of ideas or content across the community. Due to the nature of the topic, it is rather difficult to get it right the first time when defining the learning objectives, selecting the material, compiling a reader, and, more importantly, designing relevant and appropriate practical work. This leads to substantial effort (through numerous iterations) and poses risks to the course quality. This chapter attempts to support educators in the first and most crucial step in their course design: creating the syllabus. It does so by consolidating the collective experience of the authors as well as of members of the Empirical Software Engineering community; the latter was mined through two working sessions and an online survey. Specifically, it offers a list of the fundamental building blocks for a syllabus, namely course aims, course topics, and practical assignments. The course topics are also linked to the subsequent chapters of this book, so that readers can dig deeper into those chapters and get support on teaching specific research methods or cross-cutting topics. Finally, we guide educators on how to take these building blocks as a starting point and consider a number of relevant aspects to design a syllabus to meet the needs of their own program, students, and curriculum.

Designing NLP-based solutions for requirements variability management: experiences from a design science study at Visma - Elahidoost, Parisa; Unterkalmsteiner, Michael; Fucci, Davide; Fischbach, Jannik; Liljenberg, Peter (The 30th International Conference on Requirement Engineering: Foundation for Software Quality (REFSQ), 2024)
In this industry-academia collaborative project, a team of researchers, supported by a software architect, business analyst, and test engineer explored the challenges of requirement variability in a large business software development company. Question/problem: Following the design science paradigm, we studied the problem of requirements analysis and tracing in the context of contractual documents, with a specific focus on managing requirements variability. This paper reports on the lessons learned from that experience, highlighting the strategies and insights gained in the realm of requirements variability management. Principal ideas/results: This experience report outlines the insights gained from applying design science in requirements engineering research in industry. We show and evaluate various strategies to tackle the issue of requirement variability. Contribution: We report on the iterations and how the solution development evolved in parallel with problem understanding. From this process, we derive five key lessons learned to highlight the effectiveness of design science in exploring solutions for requirement variability in contract-based environments.

Identifying Concerns When Specifying Machine Learning-Enabled Systems: A Perspective-Based Approach - Villamizar, Hugo; Kalinowski, Marcos; Lopes, Helio; Mendez, Daniel (Journal of Systems and Software, 2024)
Engineering successful machine learning (ML)-enabled systems poses various challenges from both a theoretical and a practical side. Among those challenges are how to effectively address unrealistic expectations of ML capabilities from customers, managers and even other team members, and how to connect business value to engineering and data science activities composed by interdisciplinary teams. In this paper, we present PerSpecML, a perspective-based approach for specifying ML-enabled systems that helps practitioners identify which attributes, including ML and non-ML components, are important to contribute to the overall system's quality. The approach involves analyzing 59 concerns related to typical tasks that practitioners face in ML projects, grouping them into five perspectives: system objectives, user experience, infrastructure, model, and data. Together, these perspectives serve to mediate the communication between business owners, domain experts, designers, software and ML engineers, and data scientists. The creation of PerSpecML involved a series of validations conducted in different contexts: (i) in academia, (ii) with industry representatives, and (iii) in two real industrial case studies. As a result of the diverse validations and continuous improvements, PerSpecML stands as a promising approach, poised to positively impact the specification of ML-enabled systems, particularly helping to reveal key components that would have been otherwise missed without using PerSpecML.

Industrial Challenges in Secure Continuous Development - Moyón, Fabiola; Angermeir, Florian; Mendez, Daniel (46th International Conference on Software Engineering: Software Engineering in Practice, 2024)
The intersection between security and continuous software engineering has been of great interest since the early years of the agile development movement, and it remains relevant as software development processes are more frequently guided by agility and the adoption of DevOps. Several authors have contributed studies about the framing of secure agile development and secure DevOps, motivating academic contributions to methods and practices, but also discussions around benefits and challenges. Especially the challenges captured also our interest since, for the last few years, we are conducting research on secure continuous software engineering from a more applied, practical perspective with the overarching aim to introduce solutions that can be adopted at scale. The short positioning at hands summarizes a relevant part of our endeavors in which we validated challenges with several practitioners of different roles. More than framing a set of challenges, we conclude by presenting four key research directions we identified for practitioners and researchers to delineate future work.

Industrial Practices of Requirements Engineering for ML-Enabled Systems in Brazil - Pedro Santos Alves, Antonio; Kalinowski, Marcos; Mendez, Daniel; Villamizar, Hugo; Azevedo, Kelly; Escovedo, Tatiana; Lopes, Helio (Proc. of the 38th Brazilian Symposium on Software Engineering, 2024)
[Context] In Brazil, 41% of companies use machine learning (ML) to some extent. However, several challenges have been reported when engineering ML-enabled systems, including unrealistic customer expectations and vagueness in ML problem specifications. Literature suggests that Requirements Engineering (RE) practices and tools may help to alleviate these issues, yet there is insufficient understanding of RE's practical application and its perception among practitioners. [Goal] This study aims to investigate the application of RE in developing ML-enabled systems in Brazil, creating an overview of current practices, perceptions, and problems in the Brazilian industry. [Method] To this end, we extracted and analyzed data from an international survey focused on ML-enabled systems, concentrating specifically on responses from practitioners based in Brazil. We analyzed RE-related answers gathered from 72 practitioners involved in data-driven projects. We conducted quantitative statistical analyses on contemporary practices using bootstrapping with confidence intervals and qualitative studies on the reported problems involving open and axial coding procedures. [Results] Our findings highlight distinct RE implementation aspects in Brazil's ML projects. For instance, (i) RE-related tasks are predominantly conducted by data scientists; (ii) the most common techniques for eliciting requirements are interviews and workshop meetings; (iii) there is a prevalence of interactive notebooks in requirements documentation; (iv) practitioners report problems that include a poor understanding of the problem to solve and the business domain, low customer engagement, and difficulties managing stakeholders expectations. [Conclusion] These results provide an understanding of RE-related practices in the Brazilian ML industry, helping to guide research toward improving the maturity of RE for ML-enabled systems.

Measuring the Fitness-for-Purpose of Requirements: An initial Model of Activities and Attributes - Unterkalmsteiner, Michael; Fischbach, Jannik; Fucci, Davide; Mendez, Daniel (32nd International Requirements Engineering Conference (RE@Next!), 2024)

ML-Enabled Systems Model Deployment and Monitoring: Status Quo and Problems - Kalinowski, Marcos; Mendez, Daniel; Giray, Görkem; Pedro Santos Alves, Antonio; Lavesson, Niklas; Azevedo, Kelly; Villamizar, Hugo; Escovedo, Tatiana; Lopes, Helio; Biffl, Stefan; Felderer, Michael; Wagner, Stefan; et al. (15th Software Quality Days, 2024)
[Context] Systems incorporating Machine Learning (ML) models, often called ML-enabled systems, have become commonplace. However, empirical evidence on how ML-enabled systems are engineered in practice is still limited, especially for activities surrounding ML model dissemination. [Goal] We investigate contemporary industrial practices and problems related to ML model dissemination, focusing on the model deployment and the monitoring of ML life cycle phases. [Method] We conducted an international survey to gather practitioner insights on how ML-enabled systems are engineered. We gathered a total of 188 complete responses from 25 countries. We analyze the status quo and problems reported for the model deployment and monitoring phases. We analyzed contemporary practices using bootstrapping with confidence intervals and conducted qualitative analyses on the reported problems applying open and axial coding procedures. [Results] Practitioners perceive the model deployment and monitoring phases as relevant and difficult. With respect to model deployment, models are typically deployed as separate services, with limited adoption of MLOps principles. Reported problems include difficulties in designing the architecture of the infrastructure for production deployment and legacy application integration. Concerning model monitoring, many models in production are not monitored. The main monitored aspects are inputs, outputs, and decisions. Reported problems involve the absence of monitoring practices, the need to create custom monitoring tools, and the selection of suitable metrics. [Conclusion] Our results help provide a better understanding of the adopted practices and problems in practice and support guiding ML deployment and monitoring research in a problem-driven manner.

Model-based Analysis and Specification of Functional Requirements and Tests for Complex Automotive Systems - Wiecher, Carsten; Fischbach, Jannik; Mendez, Daniel; Mandel, Constantin; Günther, Matthias; Greenyer, Joel; Wolff, Carsten; Greinert, Matthias; Dumitrescu, Roman; Albers, Albert (Systems Engineering, 2024)
The specification of requirements and tests are crucial activities in automotive development projects. However, due to the increasing complexity of automotive systems, practitioners fail to specify requirements and tests for distributed and evolving systems with complex interactions when following traditional development processes. To address this research gap, we propose a technique that starts with the early identification of validation concerns from a stakeholder perspective, which we use to systematically design tests that drive a scenario-based modeling and analysis of system requirements. To ensure complete and consistent requirements and test specifications in a form that is required in automotive development projects, we develop a Model-Based Systems Engineering (MBSE) methodology. This methodology supports system architects and test designers in the collaborative application of our technique and in maintaining a central system model, in order to automatically derive the required specifications. We evaluate our methodology by applying it at KOSTAL (Tier1 supplier) and within student projects as part of the masters program Embedded Systems Engineering. Our study corroborates that our methodology is applicable and improves existing requirements and test specification processes by supporting the integrated and stakeholder-focused modeling of product and validation systems, where the early definition of stakeholder and validation concerns fosters a problem-oriented, iterative and test-driven requirements modeling.

On Developing an Artifact-based Approach to Regulatory Requirements Engineering - Kosenkov, Oleksandr; Unterkalmsteiner, Michael; Fischbach, Jannik; Mendez, Daniel; Gorschek, Tony; Fucci, Davide (2024 IEEE 32nd International Requirements Engineering Conference Workshops (REW), 2024)
Context: Regulatory acts are a challenging source when eliciting, interpreting, and analyzing requirements. Requirements engineers often need to involve legal experts who, however, may often not be available. This raises the need for approaches to regulatory Requirements Engineering (RE) covering and integrating both legal and engineering perspectives. Problem: Regulatory RE approaches need to capture and reflect both the elementary concepts and relationships from a legal perspective and their seamless transition to concepts used to specify software requirements. No existing approach considers explicating and managing legal domain knowledge and engineering-legal coordination. Method: We conducted focus group sessions with legal researchers to identify the core challenges to establishing a regulatory RE approach. Based on our findings, we developed a candidate solution and conducted a first conceptual validation to assess its feasibility. Results: We introduce the first version of our Artifact Model for Regulatory Requirements Engineering (AM4RRE) and its conceptual foundation. It provides a blueprint for applying legal (modelling) concepts and well-established RE concepts. Our initial results suggest that artifact-centric RE can be applied to managing legal domain knowledge and engineering-legal coordination. Conclusions: The focus groups that served as a basis for building our model and the results from the expert validation both strengthen our confidence that we already provide a valuable basis for systematically integrating legal concepts into RE. This overcomes contemporary challenges to regulatory RE and serves as a basis for exposure to critical discussions in the community before continuing with the development of tool-supported extensions and large-scale empirical evaluations in practice.

Ontology-Based Capability Description of MTP Modules using AAS - Barth, Christian; Freund, Matthias; Klausmann, Tobias; Bayha, Andreas; Gerber, Kathrin (VDI-Kongress AUTOMATION 2024, 2024)

Practices, Challenges, and Opportunities When Inferring Requirements From Regulations in the FinTech Sector - An Industrial Study - Elahidoost, Parisa; Mendez, Daniel; Unterkalmsteiner, Michael; Fischbach, Jannik; Feiler, Christian; Streit, Jonathan (9th International Workshop on Empirical Requirements Engineering, 2024)
[Context and motivation]: Understanding and interpreting regulatory norms and inferring software requirements from them is a critical step towards regulatory compliance, a matter of significant importance in various industrial sectors. [Question/ problem]: However, interpreting regulations still largely depends on individual legal expertise and experience within the respective domain, with little to no systematic methodologies and supportive tools to guide this practice. In fact, research in this area is too often detached from practitioners' experiences, rendering the proposed solutions not transferable to industrial practice. As we argue, one reason is that we still lack a profound understanding of industry- and domain-specific practices and challenges. [Principal ideas/ results]: We aim to close this gap and provide such an investigation at the example of the banking and insurance domain. We conduct an industrial multi-case study as part of a long-term academia-industry collaboration with a medium-sized software development and renovation company. We explore contemporary industrial practices and challenges when inferring requirements from regulations to support more problem-driven research. Our study investigates the complexities of requirement engineering in regulatory contexts, pinpointing various issues and discussing them in detail. We highlight the gathered insights and the practical challenges encountered and suggest avenues for future research. [Contribution]: Our contribution is a comprehensive case study focused on the FinTech domain, offering a detailed understanding of the specific needs within this sector. We have identified key practices for managing regulatory requirements in software development, and have pinpointed several challenges. We conclude by offering a set of recommendations for future problem-driven research directions.

Regulatory Requirements Engineering in Large Enterprises: An Interview Study on the European Accessibility Act - Kosenkov, Oleksandr; Unterkalmsteiner, Michael; Mendez, Daniel; Fischbach, Jannik (Proc. of the 25th International Conference on Product-Focused Software Process Improvement, 2024)
Context: Regulations, such as the European Accessibility Act (EAA), impact the engineering of software products and services. Managing that impact while providing meaningful inputs to development teams is one of the emerging requirements engineering (RE) challenges. Problem: Enterprises conduct Regulatory Impact Analysis (RIA) to consider the effects of regulations on software products offered and formulate requirements at an enterprise level. Despite its practical relevance, we are unaware of any studies on this large-scale regulatory RE process. Methodology: We conducted an exploratory interview study of RIA in three large enterprises. We focused on how they conduct RIA, emphasizing cross-functional interactions, and using the EAA as an example. Results: RIA, as a regulatory RE process, is conducted to address the needs of executive management and central functions. It involves coordination between different functions and levels of enterprise hierarchy. Enterprises use artifacts to support interpretation and communication of the results of RIA. Challenges to RIA are mainly related to the execution of such coordination and managing the knowledge involved. Conclusion: RIA in large enterprises demands close coordination of multiple stakeholders and roles. Applying interpretation and compliance artifacts is one approach to support such coordination. However, there are no established practices for creating and managing such artifacts.

Requirements Quality Research Artifacts: Recovery, Analysis, and Management Guideline - Frattini, Julian; Montgomery, Lloyd; Fucci, Davide; Unterkalmsteiner, Michael; Mendez, Daniel; Fischbach, Jannik (Journal of Systems and Software, 2024)
Requirements quality research, which is dedicated to assessing and improving the quality of requirements specifications, is dependent on research artifacts like data sets (containing information about quality defects) and implementations (automatically detecting and removing these defects). However, recent research exposed that the majority of these research artifacts have become unavailable or have never been disclosed, which inhibits progress in the research domain. In this work, we aim to improve the availability of research artifacts in requirements quality research. To this end, we (1) extend an artifact recovery initiative, (2) empirically evaluate the reasons for artifact unavailability using Bayesian data analysis, and (3) compile a concise guideline for open science artifact disclosure. Our results include 10 recovered data sets and 7 recovered implementations, empirical support for artifact availability improving over time and the positive effect of public hosting services, and a pragmatic artifact management guideline open for community comments. With this work, we hope to encourage and support adherence to open science principles and improve the availability of research artifacts for the requirements research quality community.

Schlüssel zur erfolgreichen Digitalisierung in Industrie 4.0 – Entwicklung, Implementierung und Nutzen digitaler Zwillinge - Knoch, Sönke; Schöttke, Dirk; Bayha, Andreas (Industry 4.0 Science, 2024)
Wandelbare Produktionsumgebungen weisen aufgrund ihrer flexiblen Funktionalität und ihres variablen Anlagendesigns eine hohe Komplexität auf. Industrie 4.0 schlägt Methoden und Werkzeuge vor, um dieser Komplexität mit einer intensiven Vernetzung aller Teilnehmer, erhöhter Transparenz durch Sensordaten sowie Assistenz und dezentralen Entscheidungen zu begegnen. Eine Schlüsselrolle nehmen dabei Digitale Zwillinge ein, die mit der Open Source Software Eclipse BaSyx realisiert werden können. Der Artikel beschreibt die Voraussetzung für einen Einstieg in die Thematik und schlüsselt die Industrie 4.0-Konzepte in einem Anwendungsfall auf.
Keywords: MbSE; Model-based Systems Engineering

Static and Dynamic Analysis of a Usage Control System - Schöpp, Ulrich; Faghih, Fathiyeh; Bandopadhyay, Subhajit; Joumaa, Hussein; Ibrahim, Amjad; Xu, Chuangjie; Ye, Xin; Dimitrakos, Theo (SACMAT 2024: Proceedings of the 29th ACM Symposium on Access Control Models and Technologies, 2024)

Systematic Mapping Study on Requirements Engineering for Regulatory Compliance of Software Systems - Kosenkov, Oleksandr; Elahidoost, Parisa; Gorschek, Tony; Fischbach, Jannik; Mendez, Daniel; Unterkalmsteiner, Michael; Fucci, Davide; Mohanani, Rahul (Information and Software Technology, 2024)
Context: As the diversity and complexity of regulations affecting Software-Intensive Products and Services (SIPS) is increasing, software engineers need to address the growing regulatory scrutiny. As with any other non-negotiable requirements, SIPS compliance should be addressed early in SIPS engineering - i.e., during requirements engineering (RE). Objectives: In the conditions of the expanding regulatory landscape, existing research offers scattered insights into regulatory compliance of SIPS. This study addresses the pressing need for a structured overview of the state of the art in software RE and its contribution to regulatory compliance of SIPS. Method: We conducted a systematic mapping study to provide an overview of the current state of research regarding challenges, principles and practices for regulatory compliance of SIPS related to RE. We focused on the role of RE and its contribution to other SIPS lifecycle phases. We retrieved 6914 studies published from 2017 until 2023 from four academic databases, which we filtered down to 280 relevant primary studies. Results: We identified and categorized the RE-related challenges in regulatory compliance of SIPS and their potential connection to six types of principles and practices. We found that about 13.6% of the primary studies considered the involvement of both software engineers and legal experts. About 20.7% of primary studies considered RE in connection to other process areas. Most primary studies focused on a few popular regulation fields and application domains. Our results suggest that there can be differences in terms of challenges and involvement of stakeholders across different fields of regulation. Conclusion: Our findings highlight the need for an in-depth investigation of stakeholders' roles, relationships between process areas, and specific challenges for distinct regulatory fields to guide research and practice.

Taxing Collaborative Software Engineering - Dorner, Michael; Smite, Darja; Zabardast, Ehsan; Mendez, Daniel; Wnuk, Krzysztof; Capraro, Maximilian; Treidler, Oliver; Kunz, Tom-Eric (IEEE Software, 2024)
The engineering of complex software systems is often the result of a highly collaborative effort. However, collaboration within a multinational enterprise has an overlooked legal implication when developers collaborate across national borders: It is taxable. In this article, we discuss the unsolved problem of taxing collaborative software engineering across borders. We (1) introduce the reader to the basic principle of international taxation, (2) identify three main challenges for taxing collaborative software engineering making it a software engineering problem, and (3) estimate the industrial significance of cross-border collaboration in modern software engineering by measuring cross-border code reviews at a multinational software company.

Teaching Empirical Research Methods in Software Engineering: An Editorial Introduction - Mendez, Daniel; Avgeriou, Paris; Kalinowski, Marcos; bin Ali, Nauman (Handbook on Teaching Empirical Software Engineering, 2024)
Empirical Software Engineering has received much attention in recent years and became a de-facto standard for scientific practice in Software Engineering. However, while extensive guidelines are nowadays available for designing, conducting, reporting, and reviewing empirical studies, similar attention has not yet been paid to teaching empirical software engineering. Closing this gap is the scope of this edited book. In the following editorial introduction, we, the editors, set the foundation by laying out the larger context of the discipline for a positioning of the remainder of this book.

The Perspective of Agile Software Developers on Data Privacy - Peixoto, Mariana; Gorschek, Tony; Mendez, Daniel; Fucci, Davide; Silva, Carla (Journal of Software: Evolution and Process, 2024)
Recent studies have shown that many software developers do not have sufficient knowledge and understanding of how to develop a privacy-friendly system. This may become a challenge in developing systems complying with data protection laws. To address this issue, we investigated the factors that influence developers' decision-making when developing privacy-sensitive systems. We conducted an empirical study by means of a survey with 109 practitioners. Our data analysis is based on the principles of social cognitive theory, which includes personal, behavioral, and external environmental factors. We identified six personal, five behavioral, and five external environment factors that affect how developers make decisions regarding privacy, including confusion between privacy and security and reliance on informal practices and organizational support gaps. These findings contribute to understanding how practitioners and companies consider privacy, showing improvements in formal training and structured support over previous studies yet highlighting persistent challenges in consistent privacy integration.
Keywords: empirical study; privacy; software development

The Perspective of Agile Software Developers on Data Privacy - Peixoto, Mariana; Gorschek, Tony; Mendez, Daniel; Silva, Carla; Fucci, Davide (Journal of Software: Evolution and Process, 2024)
Recent studies have shown that many software developers do not have sufficient knowledge and understanding of how to develop a privacy-friendly system. This may become a challenge in developing systems complying with data protection laws. To address this issue, we investigated the factors that influence developers' decision-making when developing privacy-sensitive systems. We conducted an empirical study by means of a survey with 109 practitioners. Our data analysis is based on the principles of social cognitive theory, which includes personal, behavioral, and external environmental factors. We identified six personal, five behavioral, and five external environment factors that affect how developers make decisions regarding privacy, including confusion between privacy and security and reliance on informal practices and organizational support gaps. These findings contribute to understanding how practitioners and companies consider privacy, showing improvements in formal training and structured support over previous studies yet highlighting persistent challenges in consistent privacy integration.
Keywords: empirical study; privacy; software development

The Upper Bound of Information Diffusion in Code Review - Dorner, Michael; Mendez, Daniel; Wnuk, Krzysztof; Zabardast, Ehsan; Czerwonka, Jacek (Empirical Software Engineering, 2024)
Code review, the discussion around a code change among humans, forms a communication network that enables its participants to exchange and spread information. Although reported by qualitative studies, our understanding of the capability of code review as a communication network is still limited. Objective: In this article, we report on a first step towards evaluating the capability of code review as a communication network by quantifying how fast and how far information can spread through code review: the upper bound of information diffusion in code review. Method: In an in-silico experiment, we simulate an artificial information diffusion within large (Microsoft), mid-sized (Spotify), and small code review systems (Trivago) modelled as communication networks. We then measure the minimal topological and temporal distances between the participants to quantify how far and how fast information can spread in code review. Results: An average code review participants in the small and mid-sized code review systems can spread information to between 72% and 85% of all code review participants within four weeks independently of network size and tooling; for the large code review systems, we found an absolute boundary of about 11000 reachable participants. On average (median), information can spread between two participants in code review in less than five hops and less than five days. Conclusion: We found evidence that the communication network emerging from code review scales well and spreads information fast and broadly, corroborating the findings of prior qualitative work. The study lays the foundation for understanding and improving code review as a communication network.

Towards Automated Continuous Security Compliance - Angermeir, Florian; Fischbach, Jannik; Moyón, Fabiola; Mendez, Daniel (Proceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, 2024)
Context: Continuous Software Engineering is increasingly adopted in highly regulated domains, raising the need for continuous compliance. Adherence to especially security regulations – a major concern in highly regulated domains – renders Continuous Security Compliance of high relevance to industry and research. Problem: One key barrier to adopting continuous software engineering in the industry is the resource-intensive and error-prone nature of traditional manual security compliance activities. Automation promises to be advantageous. However, continuous security compliance is under-researched, precluding an effective adoption. Contribution: We have initiated a long-term research project with our industry partner to address these issues. In this manuscript, we make three contributions: (1) We provide a precise definition of the term continuous security compliance aligning with the state-of-art, (2) elaborate a preliminary overview of challenges in the field of automated continuous security compliance through a tertiary literature study, and (3) present a research roadmap to address those challenges via automated continuous security compliance.

Two is Better Than One: Digital Siblings to Improve Autonomous Driving Testing - Matteo Biagiola,; Stocco, Andrea; Vincenzo Riccio,; Paolo Tonella, (Empirical Software Engineering, 2024)
[Invited Journal-first track at ICSE 2025]

IoT and Edge Computing EU funded projects landscape release v2.0 - Kopertowski, Zbigniew; Karagianis, Georgios (AIOTI WG Standardisation, 2023)
Keywords: IIoT

Towards a Unified Framework of Contrastive Learning for Disentangled Representations - Matthes, Stefan; Han, Zhiwei; Shen, Hao (the Thirty-seventh Annual Conference on Neural Information Processing Systems (NeurIPS 23), 2023)
Contrastive learning has recently emerged as a promising approach for learning data representations that discover and disentangle the explanatory factors of the data. Previous analyses of such approaches have largely focused on individual contrastive losses, such as noise-contrastive estimation (NCE) and InfoNCE, and rely on specific assumptions about the data generating process. This paper extends the theoretical guarantees for disentanglement to a broader family of contrastive methods, while also relaxing the assumptions about the data distribution. Specifically, we prove identifiability of the true latents for four contrastive losses studied in this paper, without imposing common independence assumptions. The theoretical findings are validated on several benchmark datasets. Finally, practical limitations of these methods are also investigated.

OntoSustain: Towards an Ontology for Corporate Sustainability Reporting - Zhou, Yuchen; Perzylo, Alexander (International Semantic Web Conference (ISWC), 2023)
The sustainability reporting of small and medium-sized enterprises is gaining significance, as their business partners often rely on them to ensure compliance and promote sustainability along the supply chain. However, the implementation of reporting remains challenging, as companies may lack the appropriate human resources to comprehend the informal textual descriptions from various reporting standards. In order to support the sustainability reporting of the companies, we present our ongoing work on the OntoSustain ontology. OntoSustain models sustainability domain knowledge, and offers good comprehensibility, transparency, and reusability for companies’ sustainability officers in data collection and indicator value derivation.
Keywords: peng; diprolea

Neurorobotic reinforcement learning for domains with parametrical uncertainty - Amaya, Lana; von Arnim, Axel (Frontiers in Neurorobotics, 2023)
Neuromorphic hardware paired with brain-inspired learning strategies have enormous potential for robot control. Explicitly, these advantages include low energy consumption, low latency, and adaptability. Therefore, developing and improving learning strategies, algorithms, and neuromorphic hardware integration in simulation is a key to moving the state-of-the-art forward. In this study, we used the neurorobotics platform (NRP) simulation framework to implement spiking reinforcement learning control for a robotic arm. We implemented a force-torque feedback-based classic object insertion task (“peg-in-hole”) and controlled the robot for the first time with neuromorphic hardware in the loop. We therefore provide a solution for training the system in uncertain environmental domains by using randomized simulation parameters. This leads to policies that are robust to real-world parameter variations in the target domain, filling the sim-to-real gap.To the best of our knowledge, it is the first neuromorphic implementation of the peg-in-hole task in simulation with the neuromorphic Loihi chip in the loop, and with scripted accelerated interactive training in the Neurorobotics Platform, including randomized domains.
Keywords: domain randomization; neuromorphic computing; neurorobotics; reinforcement learning; robot control; spiking neural networks

Towards Confidentiality in Multi-Model Inconsistency Detection for Systems Engineering - Bergemann, Sebastian (2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C), 2023)
Multi-model consistency management is used to keep models with overlapping information consistent during model-based systems engineering. Checking consistency between different models requires model data to be exchanged in some form between the modeling tools. However, in industrial practice such models can contain confidential data that must not be revealed to unauthorized parties, e.g., other departments or external companies. Therefore, it is a critical problem when the consistency management tool does not respect this. We found that none of the current multi-model consistency management approaches in literature consider this problem and provide suitable protections to prevent possible confidential data theft caused by consistency checking. Hence, we propose an approach and implementation for confidentiality preservation in multi-model inconsistency detection that does not reveal confidential model data to unauthorized parties and increases the trustworthiness of the consistency checking process regarding data confidentiality. In this paper we explain the problem and propose our basic solution idea and evaluation plan for our doctoral research.
Keywords: consistency management; multi-model inconsistency detection; confidentiality; Model-based Systems Engineering; MBSE

6G visions for a sustainable and people-centric future - from communications to services, the CONASENSE perspective - C. Sofia, Rute; Prasad, Ramjee (6G Visions for a Sustainable and People-centric Future - from communications to services, 2023)
6G is currently under definition, often being addressed from a plain telecommunications perspective as an evolutionary paradigm that represents an extension of 5G. Having as its horizon 2030, 6G initiatives are being deployed across the globe, to further ignite the development of 6G services. In its philosophy core, 6G embodies the "human in the loop" principle. The research effort aimed at 6G requires an interdisciplinary approach that ignites discussion across different key technological sectors, ranging from communications to services and business cases. The contribution of this book to research in the field concerns an evolutionary and interdisciplinary design of 6G as a paradigm that can be addressed by working together the four different computational areas of CONASENSE: communications; satellites and navigation; sensing; services. This book starts with a perspective on 6G challenges towards sustainability and addressing new business models, moving on to key topics concerning communications. It ends with chapters focusing on 6G service design. This book is, therefore, envisioned to assist in developing critical thinking to back up novel networking, applications, and services towards 6G.
Keywords: 6G; IIoT; Edge; Networking; Wireless; Sensing

A Retrospective Analysis of Grey Literature for AI-Supported Test Automation - Ricca, Filippo; Marchetto, Alessandro; Stocco, Andrea (In Proceedings of the 16th International Conference on the Quality of Information and Communications Technology, 2023)
This paper provides the results of a retrospective analysis conducted on a survey of the grey literature about the perception of practitioners on the integration of artificial intelligence (AI) algorithms into Test Automation (TA) practices. Our study involved the examination of 231 sources, including blogs, user manuals, and posts. Our primary goals were to: (a) assess the generalizability of existing taxonomies about the usage of AI for TA, (b) investigate and understand the relationships between TA problems and AI-based solutions, and (c) systematically map out the existing AI-based tools that offer AI-enhanced solutions. Our analysis yielded several interesting results. Firstly, we assessed a high degree of generalization of the existing taxonomies. Secondly, we identified TA problems that can be addressed using AI-enhanced solutions integrated into existing tools. Thirdly, we found that some TA problems require broader solutions that involve multiple software testing phases simultaneously, such as test generation and maintenance. Fourthly, we discovered that certain solutions are being investigated but are not supported by existing AI-based tools. Finally, we observed that there are tools that supports different phases of TA and may have a broader outreach.
Best Paper Award
Keywords: Test Automation; Artificial Intelligence; Grey Literature

Composing Services with SMT-Based Deployment Optimization and Service Merging Heuristic - Terzimehić, Tarik; Groh, Eddie; Zoitl, Alois (28th International Conference on Emerging Technologies and Factory Automation (ETFA), 2023)
The complexity of Industry 4.0 systems makes it challenging to manually synthesize and optimize architecture level decisions, such as service deployment and composition. To address this challenge, we propose an approach that utilizes a satisfiability modulo theories (SMT) solver to (semi-)automate service deployment and composition synthesis. Thereby, we conceptualize, formalize, and implement the complete workflow from input service workflows to valid and optimized service deployments and compositions. We demonstrate the practical application of our approach in an Industry 4.0 scenario, specifically the flexible production of new products. We evaluate the effectiveness of the proposed approach by investigating its benefits in terms of engineering effort, complexity of the resulting IEC 61499-based applications, and improvement of communication coupling quality attribute (QA).
Keywords: Industry 4.0; IEC 61499; deployment synthesis; service composition; optimization; SMT; Design-Space Exploration; DSE; Model-based Systems engineering; MbSE

Government as a Platform in Practice: Commonalities and Differences Across Three European Countries - Kuhn, Peter; Maragno, Giulia; Balta, Dian; Gastaldi, Luca; Matthes, Florian (Electronic Government, Lecture Notes in Computer Science (LNCS, volume 14130), 2023)
Government as a Platform (GaaP) promises better and more efficient public services. More and more countries are applying the approach to eGovern-ment development. However, there is scant empirical evidence on how to properly implement GaaP in practice. In particular, most of the literature focusses on the adoption in individual countries. We address this gap by investigating and systematically comparing three countries with successful GaaP implementations. By means of expert interviews and analysis of public documents we are able to extract four commonalities and three differences. We discuss our results as les-sons learnt. We further contribute to theory and practice by enhancing the knowledge on GaaP approaches, providing a first basis for guidelines toward GaaP.
Keywords: peng

Interaction Patterns for Regulatory Compliance in Federated Learning - Sellami, Mahdi; Bueno Momčilović, Tomas; Kuhn, Peter; Balta, Dian (CIISR 2023: 3rd International Workshop on Current Information Security and Compliance Issues in Information Systems Research, co-located with the 18th International Conference on Wirtschaftsinformatik (WI 2023), September 18, 2023, Paderborn, Germany, 2023)
Organizations in highly regulated domains often struggle to build well-performing machine learning (ML) models due to restrictions from data protection regulation. Federated learning (FL) has recently been introduced as a potential remedy, whereby organizations share local models while keeping data on premise. Still, regulatory compliance remains challenging in FL settings: training data needs to be shared to some extent, and models can be reverse engineered or misused towards violation of data privacy by each participating organization. Guided by design science methodology, we introduce four interaction patterns that allow for compliance-by-design and trust-context-sensitive analysis of an FL system by combining different approaches to privacy preservation. We match the patterns to privacy principles and exemplify how verifiable claims about compliance at design- and operation-time FL can be generated to make all participating organizations accountable.
Keywords: peng; Federated Learning; Privacy; Compliance; Design Patterns

IoT Things to Service Matchmaking at the Edge - Bnouhanna, Nisrine; C. Sofia, Rute (Book chapter, Shaping the Future of IoT with Edge Intelligence - How Edge Computing Enables the Next Generation of IoT Applications, 2023)
This chapter debates on the use of Machine Learning (ML) to support edge-based semantic matchmaking to handle a large-scale integration of IoT data sources with IoT platforms. The chapter starts by addressing the interoperability challenges currently faced by integrators, the role of ontologies in this context. It continues with a perspective on semantic matchmaking approaches, and ML solutions that can best support a cognitive matchmaking. The chapter then covers a use case and pilots that are being developed with a new open-source middleware, TSMatch, in the context of the Horizon 2020 EFPF project, for the purpose of environmental monitoring in smart manufacturing.
Keywords: ML; IIoT; Semantic technologies; Matchmaking

Requirements for Reliable Wireless Industrial Services. Informational draft, IETF Reliable and Available Wireless Working Group - C. Sofia, Rute; Kovatsch, Matthias; M. Mendes, Paulo (IETF RAW, 2023)
Keywords: IoT; IIoT; network protocols; TSN; Wi-Fi

Shaping the Future of IoT with Edge Intelligence - How Edge Computing Enables the Next Generation of IoT Applications - C. Sofia, Rute; Soldatos, John (Shaping the Future of IoT with Edge Intelligence - How Edge Computing Enables the Next Generation of IoT Applications, 2023)
This book presents the technologies that empower edge intelligence, along with their use in novel IoT solutions. Specifically, it presents how 5G/6G, Edge AI, and Blockchain solutions enable novel IoT-based decentralized intelligence use cases at the edge of the cloud/edge/IoT continuum. Emphasis is placed on presenting how these technologies support a wide array of functional and non-functional requirements spanning latency, performance, cybersecurity, data protection, real-time performance, energy efficiency, and more. The various chapters of the book are contributed by several EU-funded projects, which have recently developed novel IoT platforms that enable the development and deployment of edge intelligence applications based on the cloud/edge paradigm. Each one of the projects employs its own approach and uses a different mix of networking, middleware, and IoT technologies. Therefore, each of the chapters of the book contributes a unique perspective on the capabilities of enabling technologies and their integration in practical real-life applications in different sectors. The book is structured in five distinct parts. Each one of the first four parts focuses on a specific set of enabling technologies for edge intelligence and smart IoT applications in the cloud/edge/IoT continuum. Furthermore, the fifth part provides information about complementary aspects of next-generation IoT technology, including information about business models and IoT skills. Specifically: The first part focuses on 5G/6G networking technologies and their roles in implementing edge intelligence applications. The second part presents IoT applications that employ machine learning and other forms of Artificial Intelligence at the edge of the network. The third part illustrates decentralized IoT applications based on distributed ledger technologies. The fourth part is devoted to the presentation of novel IoT applications and use cases spanning the cloud/edge/IoT continuum. The fifth part discusses complementary aspects of IoT technologies, including business models and digital skills.
Keywords: Edge computing; IIoT; IoT; tactile Internet; Edge-Cloud; wireless; MaaS

Software in the Manufacturing Industry: A Review of Security Challenges and Implications - Landeck, Yannick; Balta, Dian; Wimmer, Martin; Knierim, Christian (18th International Conference on Wirtschaftsinformatik, 2023)
Software defines digital infrastructures in the manufacturing industry, connecting services and computation resources to machines and devices. These infrastructures aim at increased flexibility, scalability, and a wider application portfolio for automated manufacturing processes. At the same time, the complexity of securing software increases dramatically. Threats to confidentiality, integrity, and availability of software can result in critical losses for automated industrial production and impact manufacturing companies. In order to map existing and emerging security challenges, we present the results of a hermeneutic literature review structured along abstraction levels and vertical integration of software. Based on this structure, we derive implications for academia and practice focused on system integrators, developers, and security auditors of digital infrastructures. Thereby, we discuss courses of action mapped to software security black boxes, infrastructure heterogeneity, and the adaptation of security for operational usage.
Keywords: peng

Sustainability in Food: AI-based interactions, based on data fusion for consumer protection by the government - Kuhn, Peter; Balta, Dian (Ongoing Research, Practitioners, Posters, Workshops, and Projects at EGOV-CeDEM-ePart 2023, 2023)
Sustainability-aware grocery shopping can be challenging for consumers since product information becomes more and more extensive. Especially the variety and trustworthiness of labels makes it difficult to find the most sustainable products, in particular with respect to the heterogenic and varying level of detail claimed by producers based on these labels. In order to decrease information overload and increase consumer protection, we develop a governmentdriven platform that fuses heterogeneous food data and provides chatbot-like AI services to communicate a sustainability score. We demonstrate the use and potential of the governmentgoverned platform with two prototypical interfaces. Furthermore, we present first insights from the cooperation with start-ups who integrate the data platform into their products.
Keywords: peng; kisuscheck

The EFPF Approach to Manufacturing Applications across Edge-Cloud - C. Sofia, Rute; Coutinho, Carlos; Scivoletto, Gabriele; Insolvibile, Gianluca; A. Deshmukh, Rohit; Schneider, Alexander; Damjanovic-Behrendt, Violeta; Gigante, Fernando; Wajid, Usman; Ioannidis, Dimosthenis; Mastos, Theofilos (Shaping the Future of IoT with Edge Intelligence - How Edge Computing Enables the Next Generation of IoT Applications, 2023)
Manufacturing as a Service (MaaS) refers to a set of tools and processes that can assist the shared use of networked production facilities. In the core of this paradigm is a vision where manufacturing environments shall profit from an online set of tools and services that can be tailored to the requirements coming from the different manufacturers, thus reaching a higher degree of flexibility and an increase in production efficiency. In the context of MaaS, the Horizon 2020 European Connected Factory Platform for Agile Manufacturing (EFPF) provides an operational instantiation of a large-scale MaaS across Europe, integrating a diverse set of services such as data analytics, factory connectors, and an interoperable Data Spine to proportionate a high level of automation across different shop-floors. This chapter explains the EFPF MaaS concept, going over its architectural design, and giving insight into how developers and SMEs can profit from the EFPF open-source SDK to generate new products, and how these products can be integrated into the EFPF broad marketplace. The chapter gives insight also to the different pilots developed in the project, explaining challenges faced, and proposed solutions.
Keywords: MaaS; IIoT; ML; federated platforms; SDK

Toward Government as a Platform: An Analysis Method for Public Sector Infrastructure - Kuhn, Peter; Zavolokina, Liudmila; Balta, Dian; Matthes, Florian (18th International Conference on Wirtschaftsinformatik, 2023)
Government as a Platform (GaaP) is a promising approach to the dig-ital transformation of the public sector. In practice, GaaP is realized by platform-oriented infrastructures. However, despite successful examples, the transfor-mation toward platform-oriented infrastructures remains challenging. A potential remedy is the analysis of existing public infrastructure regarding its platform ori-entation. Such an analysis can identify the gaps to an ideal platform-oriented in-frastructure and, thus, support the transformation toward it. We follow the design science research methodology to develop a four-dimensional analysis method. We do so in three iterations, and, after each iteration, evaluate the method by its application to infrastructures in practice. With regard to theory, our results sug-gest extending GaaP conceptualizations with a specific emphasis on platform principles. With regard to practice, we contribute an analysis method that creates proposals for the improvement of infrastructures and, thus, supports the transfor-mation toward GaaP.
Keywords: peng

Towards a Knowledge-Augmented Socio-Technical Assistance System for Product Engineering - Mittel, Dominik; Hubert, Andreas; Ding, Junsheng; Perzylo, Alexander (Proceedings of the IEEE International Conference on Emerging Technologies And Factory Automation (ETFA), 2023)
Digital tools for handling the whole product engineering phase are getting more and more important in the context of Industry 4.0 and an increasing product variety. However, especially in small and medium-sized enterprises, a lot of information about product development and production is stored in different documents or isolated data silos. A promising way to arrive at a solution is to model data and knowledge with ontologies and enrich it with context information. This paper presents a concept and a showcase implementation of a company-internal and personalized assistance system for an end-to-end digital product engineering process. We combine a generic and cost-efficient human assistance solution focusing on social aspects and a company-wide knowledge graph to create a seamless and highly integrated data structure that assists many stakeholders in the product engineering process, from product designers to assembly workers. As a result, more complex products can be handled and the product engineering process can be accelerated.
Keywords: peng; diprolea

Dynamic, Context-Aware Cross-Layer Orchestration of Containerized Applications - C. Sofia, Rute; Dykeman, Doug; Urbanetz, Peter; Galal, Akram; Dave, Dushyant (IEEE Access, 2023)
Container orchestration handles the semi-automated management of applications across Edge-Cloud, providing features such as autoscaling, high availability, and portability. Having been developed for Cloud-based applications, container orchestration faces challenges in the context of decentralized Edge-Cloud environments, requiring a higher degree of adaptability in the verge of mobility, heterogeneous networks, and constrained devices. In this context, this perspective paper aims at igniting discussion on the aspects that a dynamic orchestration approach should integrate to support an elastic orchestration of containerized applications. The motivation for the provided perspective focuses on proposing directions to better support challenges faced by next-generation IoT services, such as mobility or privacy preservation, advocating the use of context awareness and a cognitive, cross-layer approach to container orchestration to be able to provide adequate support to next-generation services. A proof of concept (available open source software) of the discussed concept has been implemented in a testbed composed of embedded devices.
Keywords: IIoT; Edge; CODECO; context-awareness; Kubernetes; container orchestration; IoT

Generating Event-Based Datasets for Robotic Applications Using MuJoCo-ESIM - Palinauskas, Gintautas; Amaya, Lana; Eames, Evan; Neumeier, Michael; von Arnim, Axel (Proceedings of the 2023 International Conference on Neuromorphic Systems, 2023)
Event-based cameras are cameras with high dynamic range that measure changes in light intensity at each pixel instead of capturing frames like traditional cameras. There are several event-based camera simulators for generating event-based datasets, each specialized towards a particular domain. However, none are designed specifically for robotic use-cases. This work addresses this issue using MuJoCo, a high performance physics engine; in combination with ESIM, an established event-generation method. To the authors' knowledge, this is the first robotic simulator tool for generating event-based datasets specifically designed for the robotics domain. Furthermore, to demonstrate its capabilities we generate an event-based visual dataset of industrial sockets, which is then used to train a SNN classifier.
Keywords: Neurorobotics; neuromorphic vision; ESIM; spiking classification; SLAYER; event-based dataset generation; industrial sockets; MuJoCO; spiking neural networks

Is Overreliance on AI Provoked by Study Design? - Zhang, Zelun Tony; Tong, Sven; Liu, Yuanting; Butz, Andreas (Human-Computer Interaction – INTERACT 2023, 2023)
Recent studies found that humans tend to overrely on AI when making decisions with AI support. AI explanations were often insufficient as mitigation, and sometimes even increased overreliance. However, typical AI-assisted decision-making studies consist of long series of decision tasks, potentially causing complacent behavior, and not properly reflecting many real-life scenarios. We therefore raise the question whether these findings might be favored by the design of these studies. In a first step to answer this question, we compared different study designs in an experiment and found indications that observations of overreliance might indeed be favored by common study designs. Further research is needed to clarify to what extent overreliance can be attributed to study designs rather than more fundamental human-AI interaction issues.
Keywords: human-AI interaction; AI-assisted decision-making; explainable AI; overreliance

A Knowledge-Augmented Concept for Programming by Demonstration based on Hand-Object Actions - Ding, Junsheng; Perzylo, Alexander; Zhou, Liangwei (Workshop on Working towards Ontology-based Standards for Robotics and Automation (WOSRA), IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023)
In this paper, we introduce work towards a knowledge-augmented concept for Programming by Demonstration (PbD) for industrial assembly processes. The hand-object actions in various assembly tasks are abstracted with the semantic model of grasp types based on the Web Ontology Language (OWL) containing task context from the grasp taxonomy stored in a Knowledge Base (KB). A Long Short-Term Memory (LSTM) network is used for recognizing the grasp types from the hand skeleton in human demonstrations. The semantic process description enables the generation of human assembly processes with a sequence of hand-object actions and their conversion to a robot-suitable process variant, which can be executed based on the skill description of a workcell. We showcase the concept with different assembly steps for a ball bearing as an example of matching between human grasp types and robotic skills.
Keywords: peng; vojext

Analyzing Maintenance Activities of Software Libraries - Tsakpinis, Alexandros (EASE '23: Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering, 2023)
Industrial applications heavily integrate open-source software libraries nowadays. Beyond the benefits that libraries bring, they can also impose a real threat in case a library is affected by a vulnerability but its community is not active in creating a fixing release. Therefore, I want to introduce an automatic monitoring approach for industrial applications to identify open-source dependencies that show negative signs regarding their current or future maintenance activities. Since most research in this field is limited due to lack of features, labels, and transitive links, and thus is not applicable in industry, my approach aims to close this gap by capturing the impact of direct and transitive dependencies in terms of their maintenance activities. Automatically monitoring the maintenance activities of dependencies reduces the manual effort of application maintainers and supports application security by continuously having well-maintained dependencies.

Fine Time Measurement based Time Synchronization for Multi-AP Wireless Industrial Environments - H. Mohan, Sugandh; C. Sofia, Rute (IEEE WiMob 2023, 2023)
Industrial networks rely on Ethernet and its complementary Time Sensitive Networking standards to support critical application requirements such as bounded latency and low jitter. To meet these requirements, precise time synchronization across wired devices is typically achieved across the wired devices via the Precision Time Protocol (PTP). PTP as a UDP-based protocol can synchronize wireless devices as well, provided that these guarantee hardware-based timestamping. On the other hand, Wi-Fi integrates time synchronization mechanisms better suited to its shared medium, such as the the Fine Time Measurement (FTM) protocol. Aiming at contributing to the time synchronization solution across multi-AP industrial environments, this paper proposes a hiearchical FTM-based synchronization approach capable of supporting time synchronization in a way compatible with Ethernet/TSN. The evaluation of the proposed solution carried out with the ns-3 DetNetWiFi framework, shows that it successfully synchronizes all devices within the networ with an accuracy of less than 5 μs and providing fairness in terms of balancing FTM requests in dense networks effectively.
Keywords: wireless; IIoT; FTM; Wi-Fi6

Formal Verification for Safe AI-based Flight Planning for UAVs - Bouchekir, Radouane; Guzman Cancimance, Michell; Alasdair, Cook; Haindl, Johannes; Woolnough, Riqaq (2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), 2023)
Planning and decision-making, especially the planning of collision-free paths, are an integral part of the operation of Unmanned Aerial Vehicles (UAVs). Formalisms like Temporal Plan Networks (TPN) can be used to provide optimal flight plans for UAVs. However, ensuring that the generated flight plans are safe can be a complex task, depending on the method used to generate the plans. Safe in this context means that the next planned action for the UAV does not violate safety constraints, for example, no-fly zones (NFZ). In this paper, we investigate the application of formal methods to generate metrics that could be used as assurance evidence in the argumentation of the safety of the planning component. In particular, we make use of Satisfiability Modulo Theories (SMT)-based verification to verify low-level requirements, together with Program Verification System (PVS) to check the design requirements of the AI-based planning component.
Keywords: Formal verification; theorem proving; SMT-based verification; Temporal plan network; Remotely Piloted Aircraft (RPA); Flight planning component.

Neuromorphic Optical Flow and Real-time Implementation with Event Cameras - Schnider, Yannick; Wozniak, Stanislaw; Gehrig, Mathias; Lecomte, Jules; von Arnim, Axel; Benini, Luca; Scaramuzza, Davide; Pantazi, Angeliki (IEEE Conference on Computer Vision and Pattern Recognition Workshops 2023, 2023)
Optical flow provides information on relative motion that is an important component in many computer vision pipelines. Neural networks provide high accuracy optical flow, yet their complexity is often prohibitive for application at the edge or in robots, where efficiency and latency play crucial role. To address this challenge, we build on the latest developments in event-based vision and spiking neural networks. We propose a new network architecture, inspired by Timelens, that improves the state-of-the-art self-supervised optical flow accuracy when operated both in spiking and non-spiking mode. To implement a real-time pipeline with a physical event camera, we propose a methodology for principled model simplification based on activity and latency analysis. We demonstrate high speed optical flow prediction with almost two orders of magnitude reduced complexity while maintaining the accuracy, opening the path for real-time deployments.
Keywords: neuromorphic; optical flow; event-based; C4AI

Pooling platform: A Decentralized Local Energy Market Platform Based on Clustered Prosumer's Preferences - Okwuibe, Godwin; Vinogradova, Ksenia; Klingner, Sören; Han, Zhiwei (Future Energy Solution (FES), 2023)
Local energy markets (LEMs) have been introduced in the last few decades as a bottom-up approach solution to create a competitive market for prosumers/consumers to trade their energy and have control over their energy sources. However, there is still a gap in research for prosumers/consumers willing to exchange energy at defined price- and energy-preferences. In this work, we propose an open-source LEM model for matching prosumers and consumers with the same energy and price policy in a decentralized LEM. Our model was verified of its applicability by simulating it with the data from a German community. The simulation results showed that the model was able to satisfy the energy preferences of the consumers and prosumers in the local community to an average of more than 60%. Moreover, the model also demonstrated improved performance in terms of self-sufficiency and self-consumption ratio to the community compared to trading with the central LEM.

Specifying a Usage Control System - Schöpp, Ulrich; Xu, Chuangjie; Ibrahim, Amjad; Faghih, Fathiyeh; Dimitrakos, Theo (Proceedings of the 28th ACM Symposium on Access Control Models and Technologies (SACMAT ’23), 2023)
Modern system architectures require sophisticated access and usage control mechanisms. The need stems from demanding requirements for security, data sovereignty and privacy regulations, as well as the challenges presented by architectural approaches like zero trust networking. Usage control systems provide one approach to encapsulate and manage the complexities related to access and usage control. In order to trust a usage control system, it is essential to ensure that usage control policies express the intended properties and are enforced correctly. To achieve this, we need a precise specification of the intended behavior of a usage control system. For attribute-based access control, the XACML standard is a sufficient specification of the behavior of policies. Usage control models, such as UCON, extend access control with features for continuous authorization based on mutability of attribute values. This adds significant complexity to the problem of specifying the intended behavior. In this paper, we identify challenges with specifying a practical usage control system regarding continuous control, obligations, and concurrency aspects. We describe an approach to specifying the UCON+ model of Dimitrakos et al. and outline an implementation of the specification with Answer Set Programming.

White Paper IoT and Crisis Preparedness - AIOTI FG Buildings and Communities - Pereira, Ana; Santiago, Rita; Jimenez, Patricia; Dionisio, Pietro; Karagiannidis, Lazaros; Maltezos, Evangelos; Michalis, Panagiotis; Diagourtas, Dimitris; Suciu, George; Florea, Monica; Hovsto, Asbjorn; Sven, Flemming; et al. (AIOTI white paper, 2023)
The main objective of this vision paper is to identify the steps to be followed to develop societal resilience towards short-term and long-term risks. The purpose is to showcase the best practices and insights needed to (1) SENSE, (2) RESPOND, (3) DEFEND, and (4) RECOVER within the IoT domain by involving all key stakeholders of this value chain. According to the World Health Organization1, the emergency cycle is divided into four different phases: (1) Prevent; (2) Prepare; (3) Respond, and (4) Recover. Hence, this whitepaper aims to cover the four phases of emergency management as an integrated overview of four different fields of threats: 1. Pandemics and other Health-related threats, 2. Natural Disasters, 3. Cyber Attacks, and 4. Attacks on Public Spaces.
Keywords: IIoT; IoT; Smart Communities; Health; sensing

Fine Time Measurement based Time Synchronization for Multi-AP OFDMA Wireless Industrial Environments - H. Mohan, Sugandh; C. Sofia, Rute; Wiedner, Florian; Andre, Jonas; Carle, Georg (Master thesis: Fine Time Measurement based Time Synchronization for Multi-AP OFDMA Wireless Industrial Environments, 2023)
Industrial Internet of Things (IIoT) systems require synchronized communication among devices for sensing, computing, and actuation for some specific types of traffic. These systems typically use a wired, deterministic infrastructure that offers tight synchroniza- tion, bounded latency, and low to zero packet loss. To achieve these demands, precise time synchronization across all wired networked devices is guaranteed through the use of the Precision Time Protocol (PTP) at the Local Area Network (LAN) level. Industrial networks are undergoing change with the integration of new wireless standards such as Wi-Fi 6/7. Although PTP can support time synchronization across wireless networks, it is based on a point-to-point UDP protocol. Wi-Fi, on the other hand, has specific time synchronization mechanisms that are more effective in achieving precise and tight synchronization across wireless networks. One such mechanism is the Fine Time Mea- surement (FTM) protocol. Also, with the increase in the number of wireless devices that are incorporated into these networks, it is crucial to account for new features such as multi-AP coordination which demand accurate time synchronization. Aiming at con- tributing to the time synchronization solution across multi-AP IIoT environments, this master thesis proposes a approach based on multi-level hierarchical methodology to time synchronize the devices. It also proposes a method to select a best FTM Responder to distribute time when multiple FTM responders and initiators are functioning in the network. The assessment of the multi-level hierarchical approach’s performance indi- cates that the newly implemented mechanism allows for achieving the desired level of synchronization accuracy by successfully synchronizing all devices within the network and the evaluation of the best FTM responder selection also reveals that the algorithm performs as expected.
Keywords: IIoT; Wi-Fi 6; TSN; wireless

A Toolchain for Synthesizing and Validating Safety Architectures - Dantas, Yuri Gil; Munaro, Tiziano; Cârlan, Carmen; Nigam, Vivek; Barner, Simon; Fan, Shiqing; Pretschner, Alexander; Schöpp, Ulrich; Tverdyshev, Sergey (SN Computer Science, 2023)
Autonomous vehicles handle complicated tasks that may lead to harm when performed incorrectly. These harms, in particular when caused by system faults, may be avoided by the deployment of safety architectural patterns, such as the Heterogeneous Duplex pattern. Our goal is to provide safety engineers with computer-aided support for synthesizing architectures with safety architecture patterns. To this end, we build on our previous work in which we proposed a model-based system engineering plugin to enable the model-driven approach using safety architecture patterns. This article proposes a toolchain for synthesizing the structure and switching logic of safety architectures, as well as for validating them through simulation-based fault-injection. We validate our toolchain using an industrial use-case for autonomous driving systems, namely, a Highway Pilot system.
Keywords: Model-based systems engineering; Toolchain; Safety architecture patterns; Reconfiguration; Simulation; MbSE; AutoFOCUS3; DSE; design-space exploration

Designing AI for Appropriation Will Calibrate Trust - Zhang, Zelun Tony; Liu, Yuanting; Butz, Andreas (CHI TRAIT '23: Workshop on Trust and Reliance in AI-Assisted Tasks at CHI 2023, 2023)
Calibrating users' trust on AI to an appropriate level is widely considered one of the key mechanisms to manage brittle AI performance. However, trust calibration is hard to achieve, with numerous interacting factors that can tip trust into one direction or the other. In this position paper, we argue that instead of focusing on trust calibration to achieve resilient human-AI interactions, it might be helpful to design AI systems for appropriation first, i.e. allowing users to use an AI system according to their intention, beyond what was explicitly considered by the designer. We observe that rather than suggesting end results without human involvement, appropriable AI systems tend to offer users incremental support. Such systems do not eliminate the need for trust calibration, but we argue that they may calibrate users' trust as a side effect and thereby achieve an appropriate level of trust by design.
Keywords: appropriation; artificial intelligence; iterative problem solving; incremental support; trust calibration

Handbook – A Selection of Wireless Communications Technologies for Mechanical and Plant Engineering - Nevermann, Peter; Buckhardt, Frank; Wilkomm, Daniel; Solera, Miriam; Böckenhoff, Christoph; Zahlecker, Michael; Flieger, Matthias; Blonskey, Ofer; Daniely, Gabi; C. Sofia, Rute; Bernhard, Josef; Juárez Peña, Jorge Luis; et al. (Handbook – A Selection of Wireless Communications Technologies for Mechanical and Plant Engineering, 2023)
The publication includes a selection of current and future wireless technologies suitable for industrial use, providing an overview with valuable information on their performance including application-specific references. Thus, the handbook provides a useful and compact guide for the initial evaluation of industry-ready wireless communication technologies in the machine building ecosystem from a technology-neutral perspective. — VDMA Working Group Wireless Communications for Machines ; Lyoner Str. 18 ; 60528 Frankfurt am Main, Germany ; Phone +49 69 6603-1644 ; E-Mail wcm@vdma.org ; Internet vdma.org/wireless-communications-for-machines
Keywords: IIoT; wireless; Wi-Fi6

12th CONASENSE Symposium Proceedings - C. Sofia, Rute; Prasad, Ramjee (River Publishers, 2023)
Keywords: IIoT; CONASENSE

An Analysis of Machine Learning-based Semantic Matchmaking - Karabulut, Erkan; C. Sofia, Rute (IEEE Access, 2023)
Interoperability remains to be one of the main challenges in the Internet of Things. The increasing number of IoT data sources from various vendors augments the complexity of integrating different sensors and actuators on the existing platforms, requiring human involvement and becoming error prone. To improve this situation, devices are usually coupled with a semantic description of their attributes. Such semantic descriptions, Things Descriptions, TD , are therefore an abstraction of devices, that is helpful to achieve a smoother integration of devices into IoT platforms. However, TD are usually vendor-based, so for large-scale IoT infrastructures, the integration complexity increases, as there will be different descriptions of similar sensors, provided by different vendors to be interconnected into IoT platforms. In this context, the paper assesses different ML-based semantic matchmaking approaches, against a sentence-based statistical similarity approach. For the ML approaches, the paper focuses on clustering and Natural Language Processing. The three approaches have been implemented on a realistic testbed, and experiments carried out show that the best performance achieved in terms of accuracy, time to completion of a matchmaking request, and memory usage is the NLP-based approach.
Keywords: IIoT; ML; semantics; matchmaking; interoperability

An Ontology-Based Capability Description of MTP Modules - Barth, Christian; Klausmann, Tobias; Bayha, Andreas; Freund, Matthias; Gerber, Kathrin (preprint, 2023)
The MTP is an emerging standard for the software integration of process modules into a control system. The core concept of MTP is to separate process plants into autonomous modules called PEA which offer easy to use high level services. Applying the MTP concept leads to a modular production which can easily be adapted by adding, subtracting, or exchanging individual units. In order to leverage this flexibility, it is important to know the capabilities each PEA offers and expose them in a machine-readable way via its digital twin. As one realization of this requirement, this paper describes how a semantic annotation of MTP services using an ontology can be realised in conjunction with the AAS technology.

Implementing a Model-based Engineering Tool as Web Application - Hölzl, Florian; Barner, Simon (arXiv:2302.14091 [cs.SE], 2023)
This paper reports on a study of transferring a desktop-based model-based engineering tool to a web application. The study has been conducted in the WEBMODEL project where the well-established technology stack around the Eclipse platform and the Eclipse Modeling Framework was lifted into a cloud-based environment. As results, a modeling language independent tooling kernel for web-based modeling tools and a minimal prototypical web-based implementation of the AutoFOCUS 3 model-based engineering tool are presented. Furthermore, the report documents experiences and implementation advises gained during the implementation.
Keywords: Model-based Systems Engineering; MbSE; Tooling; Web Technology; AutoFOCUS3; AF3

Resilience Through Appropriation: Pilots' View on Complex Decision Support - Zhang, Zelun Tony; Storath, Cara; Liu, Yuanting; Butz, Andreas (28th International Conference on Intelligent User Interfaces (IUI '23), 2023)
Intelligent decision support tools (DSTs) hold the promise to improve the quality of human decision-making in challenging situations like diversions in aviation. To achieve these improvements, a common goal in DST design is to calibrate decision makers' trust in the system. However, this perspective is mostly informed by controlled studies and might not fully reflect the real-world complexity of diversions. In order to understand how DSTs can be beneficial in the view of those who have the best understanding of the complexity of diversions, we interviewed professional pilots. To facilitate discussions, we built two low-fidelity prototypes, each representing a different role a DST could assume: (a) actively suggesting and ranking airports based on pilot-specified criteria, and (b) unobtrusively hinting at data points the pilot should be aware of. We find that while pilots would not blindly trust a DST, they at the same time reject deliberate trust calibration in the moment of the decision. We revisit appropriation as a lens to understand this seeming contradiction as well as a range of means to enable appropriation. Aside from the commonly considered need for transparency, these include directability and continuous support throughout the entire decision process. Based on our design exploration, we encourage to expand the view on DST design beyond trust calibration at the point of the actual decision.
Keywords: human-AI interaction; decision support tools; intelligent decision support; AI-assisted decision-making; naturalistic decision-making; imperfect AI; appropriation; aviation

Safety-Aware Deployment Synthesis and Trade-Off Analysis of Apollo Autonomous Driving Platform - Terzimehić, Tarik; Barner, Simon; Dantas, Yuri Gil; Schöpp, Ulrich; Nigam, Vivek; Ke, Pei (9th International Workshop on Automotive System/Software Architectures (WASA) co-located with ICSA 2023, 2023)
The adoption of autonomous cars requires operational critical functions even in the event of HW faults and/or SW defects, and protection of safety-critical functions against security threats. Defining appropriate safe and secure architectures is challenging and costly. In previous work, we have proposed tools to automate the recommendation of safety and security patterns for safety-critical systems. However, safety and security measures may (negatively) influence system performance, besides introducing additional development effort. We present a design space exploration approach, a model-based engineering workflow and tool prototype for automated guidance on trade-off decisions when applying safety and security patterns on a given (unsafe) baseline architecture. Based on models that abstract the vehicle’s functionality and its software and hardware components, as well as an engine for the automated pattern recommendation, we investigate the optimization of HW/SW deployments, and provide a trade-off analysis for different architecture candidates. We implemented our approach in an open-source tool and evaluate it with a model of the Apollo autonomous driving platform.
Keywords: Autonomous vehicles; Apollo; model-driven development; system architecture; safety; model-based systems engineering; MbSE; design-space exploration; DSE; AutoFOCUS3; AF3

Automating Safety and Security Co-Design through Semantically-Rich Architecture Patterns - Dantas, Yuri Gil; Nigam, Vivek (Transactions on Cyber-Physical Systems, 2023)
During the design of safety-critical systems, safety and security engineers make use of architecture patterns, such as Watchdog and Firewall, to address identified failures and threats. Often, however, the deployment of safety architecture patterns has consequences on security; e.g., the deployment of a safety architecture pattern may lead to new threats. The other way around may also be possible; i.e., the deployment of a security architecture pattern may lead to new failures. Safety and security co-design is, therefore, required to understand such consequences and tradeoffs in order to reach appropriate system designs. Currently, architecture pattern descriptions, including their consequences, are described using natural language. Therefore, their deployment in system design is carried out manually by experts and thus is time-consuming and prone to human error, especially given the high system complexity. We propose the use of semantically rich architecture patterns to enable automated support for safety and security co-design by using Knowledge Representation and Reasoning (KRR) methods. Based on our domain-specific language, we specify reasoning principles as logic specifications written as answer-set programs. KRR engines enable the automation of safety and security co-engineering activities, including the automated recommendation of which architecture patterns can address failures or threats, and consequences of deploying such patterns. We demonstrate our approach on an example taken from the ISO 21434 standard.

Automating Vehicle SOA Threat Analysis using a Model-Based Methodology - Dantas, Yuri Gil; Nigam, Vivek; Schöpp, Ulrich; Barner, Simon; Ke, Pei (Proceedings of the 9th International Conference on Information Systems Security and Privacy (ICISSP), 2023)
This article proposes automated methods for threat analysis using a model-based engineering methodology that provides precise guarantees with respect to safety goals. This is accomplished by proposing an intruder model for automotive SOA which together with the system architecture and the loss scenarios identified by safety analysis are used as input for computing assets, impact rating, damage/threat scenarios, and attack paths. To validate the proposed methodology, we developed a faithful model of the autonomous driving functions of the Apollo framework, a widely used open source autonomous driving stack. The proposed machinery automatically enumerates several attack paths on Apollo, including attack paths not reported in the literature.
Keywords: automotive; threat analysis; service-oriented architectures; Apollo; automation; safe and secure-by-design; MbSE; Model-based Systems Engineering; AutoFOCUS3; AF3

Soziotechnisches Assistenzsystem zur lernförderlichen Arbeitsgestaltung in der robotergestützten Montage - Albu-Schaeffer, Alin; Huchler, Norbert; Kessler, Ingmar; Lay, Florian; Perzylo, Alexander; Seidler, Michael; Steinmetz, Franz; Weitschat, Roman (Gruppe. Interaktion. Organisation. Zeitschrift für Angewandte Organisationspsychologie (GIO), 2023)
Dieser Beitrag der Zeitschrift Gruppe. Interaktion. Organisation. (GIO) widmet sich der lernförderlichen Gestaltung eines roboterbasierten Assistenzsystems für industrielle Montagetätigkeiten. Individualisierte Produkte, kleinere Losgrößen und beschleunigte Prozesse sind Aspekte des digitalen Wandels in der industriellen Fertigung und Teil des Leitbilds einer flexiblen Produktion. Mensch-Roboter-Kollaboration und wissensbasiertes Engineering sind aktuelle Ansätze, um diesen Anforderungen gerecht zu werden. Dieser Artikel stellt an einem Anwendungsbeispiel (Verdrahtung von Schaltschränken) einen ersten Ansatz vor, wie mittels wissensbasierter Technologien (v. a. Ontologien und deren logische Interpretation) Vorschläge zur Arbeitszuteilung zwischen Menschen und Robotern sowohl nach ökonomischen als auch nach Kriterien der humanen Arbeitsgestaltung automatisiert erstellt werden können. Zum einen können für jede Aufgabe ihre Anforderungen mit den individuellen Fähigkeiten und Stärken der Beschäftigten sowie mit denen des kollaborativen Robotersystems in einem Mixed-Skill-Konzept nach betrieblichen Kennziffern (z. B. Zeit, Qualität) abgeglichen werden, um sich so einem optimalen Produktionsablauf anzunähern. Zum anderen können dabei Aspekte einer humanen Gestaltung der Mensch-Maschine-Interaktion (MMI) berücksichtigt werden, die vordringlich mit Blick auf Lernförderlichkeit zusammengeführt werden. Lernförderlichkeit in der MMI setzt Zeit, Handlungsräume und förderliche Inhalte voraus und ist zugleich eine zunehmende Notwendigkeit, um vorausschauend auf den dynamisierten Wandel von Arbeit zu reagieren. Denn gerade mit dem Technikeinsatz ist ein starker Tätigkeitswandel in Richtung Entscheidungsträger und Problemlöser verbunden, der neben Qualifizierung und Weiterbildung auch niedrigschwelliger, arbeitsintegrierter Lerngelegenheiten bedarf. Gerade die kollaborative Robotik als Schlüsseltechnologie der flexiblen Fertigung macht es nötig, neue Konzepte für die Organisation des hybriden Zusammenwirkens von Mensch und Roboter zu entwickeln. Im Folgenden wird aufbauend auf den grundlegenden Ansatz das Konzept eines technischen Demonstrators vorgestellt, welches entlang eines empirischen Fallbeispiels entwickelt wurde. Die prototypische, technische Umsetzung basiert auf einer Arbeitsumgebung mit einem Roboterarm und zugehörigen Werkzeugen, formalen semantischen Beschreibungen der Fähigkeiten und Tätigkeiten von Menschen und Robotern sowie einer intuitiven Benutzeroberfläche, unter anderem für die individuelle Anpassung der generierten Arbeitszuteilungen.
Keywords: kbse; peng; empress

An Analysis of ML-based Outlier Detection for Mobile Phone Trajectories - Melo Pereira, Francisco; C. Sofia, Rute (inProc. MDPI Future Internet, 2023)
This paper provides an analysis of two machine learning algorithms, density-based spatial clustering of applications with noise (DBSCAN) and the local outlier factor (LOF), applied in the detection of outliers in the context of a continuous framework for the detection of points of interest (PoI). This framework has as input mobile trajectories of users that are continuously fed to the framework in close to real time. Such frameworks are today still in their infancy and highly required in large-scale sensing deployments, e.g., Smart City planning deployments, where individual anonymous trajectories of mobile users can be useful to better develop urban planning. The paper’s contributions are twofold. Firstly, the paper provides the functional design for the overall PoI detection framework. Secondly, the paper analyses the performance of DBSCAN and LOF for outlier detection considering two different datasets, a dense and large dataset with over 170 mobile phone-based trajectories and a smaller and sparser dataset, involving 3 users and 36 trajectories. Results achieved show that LOF exhibits the best performance across the different datasets, thus showing better suitability for outlier detection in the context of frameworks that perform PoI detection in close to real time.
Keywords: Outliers; ML; IIoT; IoT

SeCloud: Computer-Aided Support for Selecting Security Measures for Cloud Architectures - Dantas, Yuri Gil; Schöpp, Ulrich (Proceedings of the 9th International Conference on Information Systems Security and Privacy (ICISSP 2023), 2023)
The adoption of cloud infrastructures requires the deployment of security measures to protect assets against threats (e.g., tampering). Several security measures/technologies are available for securing cloud infrastructures, such as Service Mesh Istio and OpenID Connect. In the current state of practice, the selection of security measures is manually done by an expert (e.g., a security engineer). It becomes challenging for experts to make these selections due to the complexity of cloud infrastructures and the vast number of available security measures and technologies. This article proposes a tool for automating the recommendation of security measures for cloud architectures. Our tool expects as input information both the cloud architecture and assets identified during a threat analysis, and recommends security measures for protecting such assets against threats. We validate our tool in a case study that provides cloud services for unmanned air vehicles (UAVs).

An initial Theory to Understand and Manage Requirements Engineering Debt in Practice - Frattini, Julian; Fucci, Davide; Mendez, Daniel; Mandic, Vladimir; Gonzalez-Huerta, Javier; Ovais Ahmad, Muhammad; Spinola, Rodrigo; Tausan, Nebojsa (Information and Software Technology, 2023)
Context: Advances in technical debt research demonstrate the benefits of applying the financial debt metaphor to support decision-making in software development activities. Although decision-making during requirements engineering has significant consequences, the debt metaphor in requirements engineering is inadequately explored. Objective: We aim to conceptualize how the debt metaphor applies to requirements engineering by organizing concepts related to practitioners' understanding and managing of requirements engineering debt (RED). Method: We conducted two in-depth expert interviews to identify key requirements engineering debt concepts and construct a survey instrument. We surveyed 69 practitioners worldwide regarding their perception of the concepts and developed an initial analytical theory. Results: We propose a RED theory that aligns key concepts from technical debt research but emphasizes the specific nature of requirements engineering. In particular, the theory consists of 23 falsifiable propositions derived from the literature, the interviews, and survey results. Conclusions: The concepts of requirements engineering debt are perceived to be similar to their technical debt counterpart. Nevertheless, measuring and tracking requirements engineering debt are immature in practice. Our proposed theory serves as the first guide toward further research in this area.

An Investigation of Causes and Effects of Trust in Boundary Artefacts - Fagerholm, Fabian; Mendez, Daniel; Ouriques, Raquel; Gislason Bern, Baldvin (Information and Software Technology, 2023)
Context: Boundary Artefacts (BAs) support software development activities in many aspects because it carries lots of information in the same object that can be used and interpreted by several social groups within an organisation. When the BAs are inconsistent regarding their content, such as many meanings or lack of contextual information, their efficiency is reduced because stakeholders will not trust them. Objective: This study aimed to understand the implications of differences in the perception of trust on software projects and their influence on stakeholders’ behaviour. Methods: We conducted an exploratory case study to observe the creation and utilisation of one specific BA and the implications of differences in trust and their influence on stakeholders’ behaviour. Results: Our investigation has shown that practitioners adding and adjusting existing content do not entirely understand the stakeholders’ needs. Together with the partial management of the content, trust is impacted. When the content of BAs does not meet the trust factors, specifically reliability and predictability, the stakeholders cannot execute their tasks appropriately, and several implications affect the software development project. Additionally, they create workarounds to supply their needs.

Building open government data platform ecosystems: A dynamic development approach that engages users from the start - Hein, Andreas; Engert, Martin; Schaffer, Norman; Krcmar, Helmut; Ryu, Sunghan; Hermes, Sebastian (Government Information Quarterly, 2023)
Open government data (OGD) platform ecosystems hold immense potential for promoting transparency, civic engagement, economic growth, and improved governmental offerings. The prevailing strategy to building OGD platform ecosystems follows a sequential approach where the OGD platform is built first and the ecosystem is built second, resulting in low engagement. In this paper, we derive insights into an alternative approach to developing OGD platform ecosystems from TourismData, a state-owned tourism initiative in Germany. We report on the phases between 2018 and 2022 and derive four dynamic and incremental phases from which we derive three learnings: context specificity, continuous adaptation, and organic expansion. Our findings have theoretical and practical implications for developing high-engagement OGD platform ecosystems that include and engage ecosystem actors from the start and, hence, take advantage of the generative potential of OGD. This approach illustrates the importance of developing OGD platform ecosystems with high contextual relevance to ensure that data can be used to enable meaningful interactions between ecosystem actors and promote continuous adaptation and expansion.

Contemporary Challenges when Developing Cyber-Physical Systems of Systems - A Case Study - Mendez, Daniel; Gorschek, Tony; Sjöberg, Peter (Proc. 11th International Workshop on Software Engineering for Systems-of-Systems and Software Ecosystems, 2023)
Digitalization has created service business opportunities for machine manufacturing companies. But creating these cyber-physical systems of systems effectively constitutes a critical measure of success in practice. At the same time, the body of knowledge in software engineering provides little guidance on how to manage this evolution. To contribute to closing this gap in the long run via problem-driven research, we need to first understand which contemporary challenges and needs are encountered in industry. To this end, we conducted a series of semi-structured interviews at a machine manufacturing company, that is expanding its service offerings utilizing digitalization, where we explored how such systems are engineered and what challenges and needs are encountered.

Let’s Stop Building at the Feet of Giants: Recovering unavailable Requirements Quality Artifacts - Frattini, Julian; Montgomery, Lloyd; Fucci, Davide; Fischbach, Jannik; Unterkalmsteiner, Michael; Mendez, Daniel (Proc. 6 Workshop on Natural Language Processing for Requirements Engineering, 2023)
Requirements quality literature abounds with publications presenting artifacts, such as data sets and tools. However, recent systematic studies show that more than 80% of these artifacts have become unavailable or were never made public, limiting reproducibility and reusability. In this work, we report on an attempt to recover those artifacts. To that end, we requested corresponding authors of unavailable artifacts to recover and disclose them according to open science principles. Our results, based on 19 answers from 35 authors (54% response rate), include an assessment of the availability of requirements quality artifacts and a breakdown of authors' reasons for their continued unavailability. Overall, we improved the availability of seven data sets and seven implementations.

Need-driven decision-making and prototyping for DLT: Framework and web-based tool - Bueno Momčilović, Tomas; Buchinger, Matthias; Balta, Dian (arXiv, 2023)
In its 14 years, distributed ledger technology has attracted increasing attention, investments, enthusiasm, and user base. However, ongoing doubts about its usefulness and recent losses of trust in prominent cryptocurrencies have fueled deeply skeptical assessments. Multiple groups attempted to disentangle the technology from the associated hype and controversy by building workflows for rapid prototyping and informed decision-making, but their mostly isolated work leaves users only with fewer unclarities. To bridge the gaps between these contributions, we develop a holistic analytical framework and open-source web tool for making evidence-based decisions. Consisting of three stages - evaluation, elicitation, and design - the framework relies on input from the users' domain knowledge, maps their choices, and provides an output of needed technology bundles. We apply it to an example clinical use case to clarify the directions of our contribution charts for prototyping, hopefully driving the conversation towards ways to enhance further tools and approaches.
Keywords: peng; diprolea

Requirements Quality Research: a harmonized Theory, Evaluation, and Roadmap - Mendez, Daniel; Frattini, Julian; Montgomery, Lloyd; Fischbach, Jannik; Fucci, Davide; Unterkalmsteiner, Michael (Requirements Engineering Journal, 2023)
High-quality requirements minimize the risk of propagating defects to later stages of the software development life cycle. Achieving a sufficient level of quality is a major goal of requirements engineering. This requires a clear definition and understanding of requirements quality. Though recent publications make an effort at disentangling the complex concept of quality, the requirements quality research community lacks identity and clear structure which guides advances and puts new findings into an holistic perspective. In this research commentary we contribute (1) a harmonized requirements quality theory organizing its core concepts, (2) an evaluation of the current state of requirements quality research, and (3) a research roadmap to guide advancements in the field. We show that requirements quality research focuses on normative rules and mostly fails to connect requirements quality to its impact on subsequent software development activities, impeding the relevance of the research. Adherence to the proposed requirements quality theory and following the outlined roadmap will be a step towards amending this gap.

Status Quo and Problems of Requirements Engineering for Machine Learning: Results from an International Survey - Kalinowski, Marcos; Mendez, Daniel; Villamizar, Hugo; Biffl, Stefan; Felderer, Michael; Wagner, Stefan; Gorschek, Tony; Pedro Santos Alves, Antonio; Giray, Görkem; Baldassarre, Teresa; Musil, Jürgen; Lavesson, Niklas; et al. (24th International Conference on Product-Focused Software Process Improvement, 2023)
Systems that use Machine Learning (ML) have become commonplace for companies that want to improve their products and processes. Literature suggests that Requirements Engineering (RE) can help address many problems when engineering ML-enabled systems. However, the state of empirical evidence on how RE is applied in practice in the context of ML-enabled systems is mainly dominated by isolated case studies with limited generalizability. We conducted an international survey to gather practitioner insights into the status quo and problems of RE in ML-enabled systems. We gathered 188 complete responses from 25 countries. We conducted quantitative statistical analyses on contemporary practices using bootstrapping with confidence intervals and qualitative analyses on the reported problems involving open and axial coding procedures. We found significant differences in RE practices within ML projects. For instance, (i) RE-related activities are mostly conducted by project leaders and data scientists, (ii) the prevalent requirements documentation format concerns interactive Notebooks, (iii) the main focus of non-functional requirements includes data quality, model reliability, and model explainability, and (iv) main challenges include managing customer expectations and aligning requirements with data. The qualitative analyses revealed that practitioners face problems related to lack of business domain understanding, unclear goals and requirements, low customer engagement, and communication issues. These results help to provide a better understanding of the adopted practices and of which problems exist in practical environments. We put forward the need to adapt further and disseminate RE-related practices for engineering ML-enabled systems.

The Role of Socially Aware Networking in Supporting 6G IoT - C. Sofia, Rute (6G Visions for a Sustainable and People-centric Future: From Communications to Services, the CONASENSE Perspective, 2023)
Keywords: IIoT; IoT; 6G; Edge

Connected vehicle simulation framework for parking occupancy prediction (demo paper) - Resce, Pierpaolo; Vorwerk, Lukas; Han, Zhiwei; Cornacchia, Giuliano; Isfahani Alamdari, Omid; Nanni, Mirco; Pappalardo, Luca; Weimer, Daniel; Liu, Yuanting (SIGSPATIAL '22: Proceedings of the 30th International Conference on Advances in Geographic Information Systems, 2022)
This paper demonstrates a simulation framework that collects data about connected vehicles' locations and surroundings in a realistic traffic scenario. Our focus lies on the capability to detect parking spots and their occupancy status. We use this data to train machine learning models that predict parking occupancy levels of specific areas in the city center of San Francisco. By comparing their performance to a given ground truth, our results show that it is possible to use simulated connected vehicle data as a base for prototyping meaningful AI-based applications.

Fine Time Measurement Based Synchronisation for Industrial Wireless/Wired Networks - H. Mohan, Sugandh; C. Sofia, Rute (inProc. IEEE WPMC 2022, 2022)
Keywords: wireless; TSN; time synchronization; FTM; Wi-Fi6

Neue Standards erweitern deterministische drahtlose Produktionsinfrastructuren - C. Sofia, Rute (VDMA Future Manufacturing, 2022)
Keywords: IIoT; wireless; Wi-Fi6; TSN

Closing the GaaP: Lessons Learned from a Web-based Analysis Tool for Practitioners - Kuhn, Peter; Balta, Dian; Matthes, Florian (Ongoing Research, Practitioners, Posters, Workshops, and Projects at EGOV-CeDEM-ePart 2022, 2022)
Government as a Platform (GaaP) is an approach to government that enables user-friendly and efficient public services through the implementation of platform structures and principles such as modularization and co-creation. While some countries have been or are currently implementing the approach successfully, other countries experience barriers in the application of GaaP. Recent literature is attempting to address these barriers, for example by prac-tice-oriented conceptualizations of GaaP. However, those conceptualizations still need to be applied with expert knowledge. We develop a tool for practitioners that makes an exemplary GaaP concept applicable without expert knowledge. We present lessons learned from the development process and discuss implications for theory and practice.

Inferring Region Types via an Abstract Notion of Environment Transformation - Schöpp, Ulrich; Xu, Chuangjie (Asian Symposium on Programming Languages and Systems (APLAS 2022), 2022)
Region-based type systems are a powerful tool for various kinds of program analysis. We introduce a new inference algorithm for region types based on an abstract notion of environment transformation. It analyzes the code of a method only once, even when there are multiple invocations of the method of different region types in the program. Elements of such an abstract transformation are essentially constraints for equality and subtyping that capture flow information of the program. In particular, we work with access graphs in the definition of abstract transformations to guarantee the termination of the inference algorithm, because they provide a finite representation of field access paths.
to appear

Decision Framework for Improved Distributed Ledger Technology Utilization - Bueno Momčilović, Tomas; Buchinger, Matthias; Delitz, Vincent; Balta, Dian (AMCIS 2022 Proceedings, 2022)
Distributed ledger technology (DLT) has been salient in research and practice for over a decade, with substantial investments in numerous areas. Still, the absence of a rapid, industry-wide success fuels skepticism and numerous decision frameworks emerged focusing on how to scaffold DLT utilization. However, a consideration of needs, added value, and integrative design of DLT-based systems remains overlooked. By analyzing existing frameworks and DLT Proof-of Concepts, we provide a research-in-progress decision framework for making evidence-based decisions on whether to use DLT and how to design a technology bundle for specific cases. Our main contribution centers on the focus on rapid collaborative prototyping. For applicability and validation, we implement the framework in an online questionnaire-like tool that generates a detailed report as a basis for an informed decision. While beneficial for academia and practice, our framework draws clear directions for future research on complementary tools, enhanced recommendations and the design of feasible DLT solutions for real world challenges.
Keywords: odim; diprolea

Defining adaptivity and logical architecture for engineering (smart) self-adaptive cyber–physical systems - Petrovska, Ana; Kugele, Stefan; Hutzelmann, Thomas; Beffart, Theo; Bergemann, Sebastian; Pretschner, Alexander (Information and Software Technology, 2022)
Context: Modern cyber–physical systems (CPSs) are embedded in the physical world and intrinsically operate in a continuously changing and uncertain environment or operational context. To meet their business goals and preserve or even improve specific adaptation goals, besides the variety of run-time uncertainties and changes to which the CPSs are exposed—the systems need to self-adapt. Objective: The current literature in this domain still lacks a precise definition of what self-adaptive systems are and how they differ from those considered non-adaptive. Therefore, in order to answer how to engineer self-adaptive CPSs or self-adaptive systems in general, we first need to answer what is adaptivity, correspondingly self-adaptive systems. Method: In this paper, we first formally define the notion of adaptivity. Second, within the frame of the formal definitions, we propose a logical architecture for engineering decentralised self-adaptive CPSs that operate in dynamic, uncertain, and partially observable operational contexts. This logical architecture provides a structure and serves as a foundation for the implementation of a class of self-adaptive CPSs. Results: First, our results show that in order to answer if a system is adaptive, the right framing is necessary: the system’s adaptation goals, its context, and the time period in which the system is adaptive. Second, we discuss the benefits of our architecture by comparing it with the MAPE-K conceptual model. Conclusion: Commonly accepted definitions of adaptivity and self-adaptive systems are necessary for work in this domain to be compared and discussed since the same terms are often used with different semantics. Furthermore, in modern self-adaptive CPSs, which operate in dynamic and uncertain contexts, it is insufficient if the adaptation logic is specified during the system’s design, but instead, the adaptation logic itself needs to adapt and “learn” during run-time.
Keywords: Adaptivity; Quality function; Knowledge; Logical architecture; Self-adaptive cyber–physical systems; Model-based Systems Engineering; MbSE