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Data from software repositories have become an important foundation for theempirical study of software engineering processes. A recurring theme in therepository mining literature is the inference of developer networks capturinge.g. collaboration, coordination, or communication from the commit history ofprojects. Most of the studied networks are based on the co-authorship ofsoftware artefacts. Because this neglects detailed information on code changesand code ownership we introduce git2net, a scalable python software thatfacilitates the extraction of fine-grained co-editing networks in large gitrepositories. It uses text mining techniques to analyse the detailed history oftextual modifications within files. We apply our tool in two case studies usingGitHub repositories of multiple Open Source as well as a commercial softwareproject. Specifically, we use data on more than 1.2 million commits and morethan 25'000 developers to test a hypothesis on the relation between developerproductivity and co-editing patterns in software teams. We argue that git2netopens up a massive new source of high-resolution data on human collaborationpatterns that can be used to advance theory in empirical software engineering,computational social science, and organisational studies.

Analysing Time-Stamped Co-Editing Networks in Software Development Teams using git2net