@article{, author = {Fischbach, Jannik and Mendez, Daniel and Frattini, Julian and Kosenkov, Oleksandr and Elahidoost, Parisa and Adam, Max and Dzhagatspanyan, Victor}, title = {Automatic ESG Assessment of Companies by Mining and Evaluating Media Coverage Data: NLP Approach and Tool}, journal = {5th Financial Narrative Processing Workshop}, year = {2024}, abstract = {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.}, doi = {https://doi.org/10.48550/arXiv.2212.06540}, url = {https://arxiv.org/abs/2212.06540}, } @conference{, author = {Elahidoost, Parisa and Unterkalmsteiner, Michael and Fucci, Davide and Fischbach, Jannik and Liljenberg, Peter}, title = {Designing NLP-based solutions for requirements variability management: experiences from a design science study at Visma}, publisher = {Springer}, journal = {The 30th International Conference on Requirement Engineering: Foundation for Software Quality (REFSQ)}, year = {2024}, abstract = {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.}, doi = {https://doi.org/10.48550/arXiv.2402.07145}, url = {https://arxiv.org/abs/2402.07145}, } @conference{, author = {Klymenko, Oleksandra and Meisenbacher, Stephan and Kosenkov, Oleksandr and Elahidoost, Parisa and Mendez, Daniel and Matthes, Florian}, title = {Understanding the Implementation of Technical Measures in the Process of Data Privacy Compliance}, booktitle = {Proc. of the 16th International Symposium on Empirical Software Engineering and Measurement}, publisher = {IEEE}, year = {2022}, } @inproceedings{8719411, author = {Iqbal, Tahira and Elahidoost, Parisa and L{\'{u}}cio, Levi}, title = {A Bird's Eye View on Requirements Engineering and Machine Learning}, booktitle = {Proceedings of the 25th Asia-Pacific Software Engineering Conference (APSEC)}, pages = {11-20}, year = {2018}, month = dec, abstract = {Machine learning (ML) has demonstrated practical impact in a variety of application domains. Software engineering is a fertile domain where ML is helping in automating different tasks. In this paper, our focus is the intersection of software requirement engineering (RE) and ML. To obtain an overview of how ML is helping RE and the research trends in this area, we have surveyed a large number of research articles. We found that the impact of ML can be observed in requirement elicitation, analysis and specification, validation and management. Furthermore, in these categories, we discuss the specific problem solved by ML, the features and ML algorithms used as well as datasets, when available. We outline lessons learned and envision possible future directions for the domain.}, doi = {10.1109/APSEC.2018.00015}, keywords = {Requirements Engineering, Machine learning, State of the art, Overview, Model-based Systems Engineering, MbSE}, }