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Analysing Time-Stamped Co-Editing Networks in Software Development Teams using git2net
Christoph Gote1, Ingo Scholtes2, Frank Schweitzer1
1Chair of Systems Design, ETH Zurich, Zurich, Switzerland.
Empirical Software Engineering
|November 1, 2021
Summary
We introduce git2net, a tool to analyze developer collaboration through co-editing networks in software projects. This method reveals fine-grained human interaction patterns for empirical software engineering research.
Area of Science:
- Empirical Software Engineering
- Computational Social Science
- Organizational Studies
Background:
- Software repositories are crucial for empirical software engineering studies.
- Existing research often infers developer networks from commit histories, focusing on co-authorship.
- Detailed code changes and ownership data within repositories are often overlooked.
Purpose of the Study:
- To introduce git2net, a scalable Python tool for extracting fine-grained co-editing networks from large Git repositories.
- To analyze detailed textual modifications within files to understand developer collaboration.
- To investigate the relationship between developer productivity and co-editing patterns.
Main Methods:
- Developed git2net, a Python software tool for Git repository analysis.
- Utilized text mining techniques to analyze file modification histories.
- Applied the tool to two case studies involving GitHub repositories (Open Source and proprietary).
- Analyzed over 1.2 million commits and 25,000 developers.
Main Results:
- Successfully extracted fine-grained co-editing networks from large-scale software projects.
- Enabled the testing of hypotheses regarding developer productivity and collaboration patterns.
- Demonstrated the utility of git2net in analyzing human collaboration in software development.
Conclusions:
- git2net provides a novel, high-resolution data source for studying human collaboration patterns.
- The tool advances empirical software engineering, computational social science, and organizational studies.
- Co-editing network analysis offers deeper insights than traditional co-authorship studies.
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