Our research aims to help distributed and interdisciplinary software teams to collaborate more efficiently and build high-quality software systems, especially in the context of modern open-source collaboration forms, fork-based development, and interdisciplinary teams when building AI-enabled systems or scientific software. To achieve the goals, we combine advances in tooling and software engineering principles with insights from other disciplines that study human collaboration, for which we combine and mix a wide range of research methods. We discover and evaluate existing interventions and develop new ones that steer collaborative development toward better practices.


Interdisciplinary Research, Software Engineering (SE), Empirical SE, SE for AI, AI for SE, AI-based Software, Scientific Software, Collaborative Software Development, Fork/Branch/Pull-based Development, GitHub, Open Source


Our work is funded by IBM CAS, NSERC, NumFOCUS, and UofT CARTE.