@inproceedings{iqbal+19, author = {Iqbal, Tahira and Seyff, Norbert and Mendez, Daniel}, title = {Generating Requirements out of Thin Air: Towards Automated Feature Identification for New Apps}, booktitle = {Proc. of the 3rd International Workshop on Crowd-Based Requirements Engineering}, year = {2019}, month = sep, location = {South Korea}, abstract = {App store mining has proven to be a promising technique for requirements elicitation as companies can gain valuable knowledge to maintain and evolve existing apps. However, despite first advancements in using mining techniques for requirements elicitation, little is yet known how to distill requirements for new apps based on existing (similar) solutions and how exactly practitioners would benefit from such a technique. In the proposed work, we focus on exploring information (e.g. app store data) provided by the crowd about existing solutions to identify key features of applications in a particular domain. We argue that these discovered features and other related influential aspects (e.g. ratings) can help practitioners(e.g. software developer) to identify potential key features for new applications. To support this argument, we first conducted an interview study with practitioners to understand the extent to which such an approach would find champions in practice. In this paper, we present the first results of our ongoing research in the context of a larger road-map. Our interview study confirms that practitioners see the need for our envisioned approach. Furthermore, we present an early conceptual solution to discuss the feasibility of our approach. However, this manuscript is also intended to foster discussions on the extent to which machine learning can and should be applied to elicit automated requirements on crowd generated data on different forums and to identify further collaborations in this endeavor.}, url = {https://arxiv.org/abs/1909.11302}, } @article{, author = {Groher, Iris and Seyff, Norbert and Iqbal, Tahira}, title = {Towards Automatically Identifying Potential Sustainability Effects of Requirements}, journal = {8th International Workshop on Requirements Engineering for Sustainable Systems (RE4SuSy)}, year = {2019}, address = {Jeju Island, South Korea}, abstract = {Software developers are gradually becoming aware that their systems have effects on sustainability. The identification of potential effects software-intensive systems can have on different sustainability dimensions over time is yet in its infancy. Researchers are currently exploring approaches which strongly make use of expert knowledge to identify potential effects. In this work in progress paper, we are looking at the problem from a different angle: we report on the exploration of a machine learning-based approach to identify potential effects. Such an approach allows to save time and costs but increases the risk that potential effects are overseen. First results of applying the machine learning-based approach in the domain of home automation systems are promising, but also indicate that further research is needed before our approach can be applied in practice. Furthermore, we have learned that even providing the ground truth for training the algorithms is a challenging task.}, } @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}, } @inproceedings{8501293, author = {L{\'{u}}cio, Levi and Iqbal, Tahira}, title = {Formalizing EARS – First Impressions}, booktitle = {2018 1st International Workshop on Easy Approach to Requirements Syntax (EARS)}, pages = {11-13}, year = {2018}, month = aug, abstract = {The Easy Approach to Requirements Specification (EARS) has been designed primarily as a set of templates to assist requirements engineers in writing software requirements that are clear and understandable. Its target are thus requirements engineers, software architects and developers. Due to the minimalistic nature of the English sentences that make up an EARS specification, it is reasonable to expect that automated tasks can be performed on EARS specification, among which verification and code synthesis. Given English cannot be directly understood by machines without some degree of ambiguity, EARS requirements can only by automatically processed if they are translated in advance into formal specifications. In this short paper, we explore how a translation from EARS into Linear Temporal Logic can be implemented in practice.}, doi = {10.1109/EARS.2018.00009}, keywords = {EARS, Linear Temporal Logic, Translation, Formalization, Model-based Systems Engineering, MbSE}, }