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

TL;DR

This paper introduces git2net, a scalable tool for extracting detailed co-editing networks from large git repositories, enabling high-resolution analysis of developer collaboration patterns in software engineering.

Contribution

The paper presents git2net, a novel Python tool that analyzes textual code changes to generate fine-grained developer networks from large-scale git data.

Findings

Analyzed 1.2 million commits and 25,000 developers.

Identified patterns linking developer productivity and co-editing.

Demonstrated the tool's applicability in diverse projects.

Abstract

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