Michel R. V. Chaudron

Siamak Farshidi, Amir Saberhabibi, Behbod Eskafi et al.

Selecting third-party software packages in open-source ecosystems like Python is challenging due to the large number of alternatives and limited transparent evidence for comparison. Generative AI tools are increasingly used in development workflows, but their suggestions often overlook dependency evaluation, emphasize popularity over suitability, and lack reproducibility. This creates risks for projects that require transparency, long-term reliability, maintainability, and informed architectural decisions. This study formulates software package selection as a Multi-Criteria Decision-Making (MCDM) problem and proposes a data-driven framework for technology evaluation. Automated data pipelines continuously collect and integrate software metadata, usage trends, vulnerability information, and developer sentiment from GitHub, PyPI, and Stack Overflow. These data are structured into a decision model representing relationships among packages, domain features, and quality attributes. The framework is implemented in PySelect, a decision support system that uses large language models to interpret user intent and query the model to identify contextually appropriate packages. The approach is evaluated using 798,669 Python scripts from 16,887 GitHub repositories and a user study based on the Technology Acceptance Model. Results show high data extraction precision, improved recommendation quality over generative AI baselines, and positive user evaluations of usefulness and ease of use. This work introduces a scalable, interpretable, and reproducible framework that supports evidence-based software selection using MCDM principles, empirical data, and AI-assisted intent modeling.

Arif Nurwidyantoro, Mojtaba Shahin, Michel Chaudron et al.

Background: There is a growing awareness of the importance of human values (e.g., inclusiveness, privacy) in software systems. However, there are no practical tools to support the integration of human values during software development. We argue that a tool that can identify human values from software development artefacts and present them to varying software development roles can (partially) address this gap. We refer to such a tool as human values dashboard. Further to this, our understanding of such a tool is limited. Aims: This study aims to (1) investigate the possibility of using a human values dashboard to help address human values during software development, (2) identify possible benefits of using a human values dashboard, and (3) elicit practitioners' needs from a human values dashboard. Method: We conducted an exploratory study by interviewing 15 software practitioners. A dashboard prototype was developed to support the interview process. We applied thematic analysis to analyse the collected data. Results: Our study finds that a human values dashboard would be useful for the development team (e.g., project manager, developer, tester). Our participants acknowledge that development artefacts, especially requirements documents and issue discussions, are the most suitable source for identifying values for the dashboard. Our study also yields a set of high-level user requirements for a human values dashboard (e.g., it shall allow determining values priority of a project). Conclusions: Our study suggests that a values dashboard is potentially used to raise awareness of values and support values-based decision-making in software development. Future work will focus on addressing the requirements and using issue discussions as potential artefacts for the dashboard.

Dave R. Stikkolorum, Michel R. V. Chaudron, Oswald de Bruin

This paper introduces our gamification of a part of our software design curriculum. Based on typical design principles a motivating learning game is developed to train students in software design. We use Bloom's taxonomy to determine learning objectives. We keep the player engaged with direct feedback in a challenging level based game with increasing complexity. Players can evaluate their design actions with the help of the visualisation of control and data flows. The main learning objective: applying design principles, fits the game's main activity. This supports the learning by doing approach of lecturers. A user test indicates possible learning effects and a playable game.

Ramin Etemaadi, Kenneth Lind, Rogardt Heldal et al.

The goal of this technical report is to give the details of a real world existing sub-system in the automotive industry. It is produced to be used for reproduction of the same experiment if other researchers are interested in. Hence, it would be possible to compare the results of our published studies with the results of similar tools. The data is collected for the purpose of applying metaheuristic optimization approaches. The case study based on these data shows that metaheuristic optimization approaches can find efficient solutions for multiple quality attributes while fulfilling given constraints. The case study was conducted at Saab Automobile AB in order to evaluate the AQOSA framework in an industrial context. AQOSA (Automated Quality-driven Optimization of Software Architecture) is our architecture optimization framework that supports multiple quality attributes including response time, processor utilization, bus utilization, safety and cost. To enable validation of the results we selected an existing realization for the Saab 9-5 Instrument Cluster Module ECU (Electronic Control Unit) and the surrounding sub-systems. The goal of the case study is to find a better solution than the current realization while fulfilling the requirements and constraints. The results of the case study and the details of AQOSA framework is reported in a paper from the authors in Journal of Systems and Software, Special Issue on Quality Optimization of Software Architecture and Design Specifications.