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Summary
This summary is machine-generated.

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.

Keywords:
Collaboration networksDeveloper productivityRepository mining

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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.