1. Identifying collaborators in large codebases
- Author
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Long, Waren, Markovtsev, Vadim, Mougard, Hugo, Bulychev, Egor, and Hula, Jan
- Subjects
Software Engineering (cs.SE) ,Social and Information Networks (cs.SI) ,FOS: Computer and information sciences ,Computer Science - Software Engineering ,Computer Science - Machine Learning ,Statistics - Machine Learning ,Computer Science - Social and Information Networks ,Machine Learning (stat.ML) ,Machine Learning (cs.LG) - Abstract
The way developers collaborate inside and particularly across teams often escapes management's attention, despite a formal organization with designated teams being defined. Observability of the actual, organically formed engineering structure provides decision makers invaluable additional tools to manage their talent pool. To identify existing inter and intra-team interactions - and suggest relevant opportunities for suitable collaborations - this paper studies contributors' commit activity, usage of programming languages, and code identifier topics by embedding and clustering them. We evaluate our findings collaborating with the GitLab organization, analyzing 117 of their open source projects. We show that we are able to restore their engineering organization in broad strokes, and also reveal hidden coding collaborations as well as justify in-house technical decisions., Comment: 4 pages; Workshop on Machine Learning for Software Engineering 2019
- Published
- 2019
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