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Analysing Time-Stamped Co-Editing Networks in Software Development Teams using git2net
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 his...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8550015/ https://www.ncbi.nlm.nih.gov/pubmed/34720670 http://dx.doi.org/10.1007/s10664-020-09928-2 |
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author | Gote, Christoph Scholtes, Ingo Schweitzer, Frank |
author_facet | Gote, Christoph Scholtes, Ingo Schweitzer, Frank |
author_sort | Gote, Christoph |
collection | PubMed |
description | 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. Many works in this area studied networks of co-authorship of software artefacts, neglecting detailed information on code changes and code ownership available in software repositories. To address this issue, 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 proprietary 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 an important 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1007/s10664-020-09928-2). |
format | Online Article Text |
id | pubmed-8550015 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-85500152021-10-29 Analysing Time-Stamped Co-Editing Networks in Software Development Teams using git2net Gote, Christoph Scholtes, Ingo Schweitzer, Frank Empir Softw Eng Article 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. Many works in this area studied networks of co-authorship of software artefacts, neglecting detailed information on code changes and code ownership available in software repositories. To address this issue, 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 proprietary 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 an important 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1007/s10664-020-09928-2). Springer US 2021-05-26 2021 /pmc/articles/PMC8550015/ /pubmed/34720670 http://dx.doi.org/10.1007/s10664-020-09928-2 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Gote, Christoph Scholtes, Ingo Schweitzer, Frank Analysing Time-Stamped Co-Editing Networks in Software Development Teams using git2net |
title | Analysing Time-Stamped Co-Editing Networks in Software Development Teams using git2net |
title_full | Analysing Time-Stamped Co-Editing Networks in Software Development Teams using git2net |
title_fullStr | Analysing Time-Stamped Co-Editing Networks in Software Development Teams using git2net |
title_full_unstemmed | Analysing Time-Stamped Co-Editing Networks in Software Development Teams using git2net |
title_short | Analysing Time-Stamped Co-Editing Networks in Software Development Teams using git2net |
title_sort | analysing time-stamped co-editing networks in software development teams using git2net |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8550015/ https://www.ncbi.nlm.nih.gov/pubmed/34720670 http://dx.doi.org/10.1007/s10664-020-09928-2 |
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