Georgia M. Kapitsaki

Evangelia Vanezi, Georgia M. Kapitsaki, Anna Philippou

The application of the General Data Protection Regulation (GDPR) has significantly affected privacy requirements elicitation, modelling, and verification in Software Engineering (SE). One of the affected areas is requirements visualisation through modelling diagrams, which plays a crucial role in ensuring privacy compliance, as functional system requirements should be integrated with GDPR-based privacy requirements. We present a systematic literature review on how SE diagrams have been employed to capture and integrate GDPR-based privacy requirements into software system design. The study aims to identify the existing research landscape, existing gaps, and directions for future work. Following a rigorous search protocol and addressing two research questions, 18 primary studies published between 2017 and 2025 were selected, analysed, and categorised based on (i) the diagram types used, and (ii) the GDPR principles or rights addressed. The findings highlight the need for inter-diagram integration, full lifecycle traceability mechanisms, tool support, and automated compliance checking.

Maria Papoutsoglou, Georgia M. Kapitsaki, Daniel German et al.

Free and open source software is widely used in the creation of software systems, whereas many organisations choose to provide their systems as open source. Open source software carries licenses that determine the conditions under which the original software can be used. Appropriate use of licenses requires relevant expertise by the practitioners, and has an important legal angle. Educators and employers need to ensure that developers have the necessary training to understand licensing risks and how they can be addressed. At the same time, it is important to understand which issues practitioners face when they are using a specific open source license, when they are developing new open source software products or when they are reusing open source software. In this work, we examine questions posed about open source software licensing using data from the following Stack Exchange sites: Stack Overflow, Software Engineering, Open Source and Law. We analyse the indication of specific licenses and topics in the questions, investigate the attention the posts receive and trends over time, whether appropriate answers are provided and which type of questions are asked. Our results indicate that practitioners need, among other, clarifications about licensing specific software when other licenses are used, and for understanding license content. The results of the study can be useful for educators and employers, organisations that are authoring open source software licenses and developers for understanding the issues faced when using licenses, whereas they are relevant to other software engineering research areas, such as software reusability.

Georgia M. Kapitsaki

A very large number of datasets are made available via Hugging Face (HF). These datasets are used to train a vast number of AI models, also available via HF. There are regulatory issues that might arise in the AI supply chain when those datasets do not follow, for instance, data privacy practices, or do not disclose their data sources, cleaning and preparation processes. In this preliminary work, regulatory compliance-readiness is examined as a quality attribute in the framework of HF datasets, with a main focus on data privacy (e.g. GDPR, CCPA). Towards this direction, an analysis on regulatory compliance (e.g. with privacy laws) of the dataset using the dataset card and the dataset structure overview as starting points has been performed. We collected 11,682 HF datasets that have a dataset card, are not gated and have at least 500 downloads and analyzed the datasets using automated techniques and manual analysis on a sample of the dataset. The results show that a very small number of datasets are explicit on the datasets creation processes and mention data sources, while some make mentions to Personally Identifiable Information (PII) or other sensitive data in their data schemas. These results show the need for more detailed examinations of regulatory compliance in AI datasets and models, and research on tools that provide guidance to practitioners and automate the compliance process.

Maria Papoutsoglou, Johannes Wachs, Georgia M. Kapitsaki

Software developers are social creatures: they communicate, collaborate, and promote their work in a variety of channels. Twitter, GitHub, Stack Overflow, and other platforms offer developers opportunities to network and exchange ideas. Researchers analyze content on these sites to learn about trends and topics in software engineering. However, insight mined from the text of Stack Overflow questions or GitHub issues is highly focused on detailed and technical aspects of software development. In this paper, we present a relatively new online community for software developers called DEV. On DEV users write long-form posts about their experiences, preferences, and working life in software, zooming out from specific issues and files to reflect on broader topics. About 50,000 users have posted over 140,000 articles related to software development. In this work, we describe the content of posts on DEV using a topic model, showing that developers discuss a rich variety and mixture of social and technical aspects of software development. We show that developers use DEV to promote themselves and their work: 83% link their profiles to their GitHub profiles and 56% to their Twitter profiles. 14% of users pin specific GitHub repos in their profiles. We argue that DEV is emerging as an important hub for software developers, and a valuable source of insight for researchers to complement data from platforms like GitHub and Stack Overflow.