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How do developers collaborate? Investigating GitHub heterogeneous networks

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Abstract

Assessing the collaboration among developers is important to understand different aspects of software lifecycle including code smell intensity, bug fixes, and software quality. This kind of collaboration can be obtained from social networks, which represent interactions between individuals in different contexts. In this paper, we model GitHub developers’ collaborations in a heterogeneous network by considering three aspects: social collaboration, collaboration time in a repository and technical features. Then, we explore the GitHub network from different perspectives: size, relevance, and potential applications. The results show the considered metrics are not correlated, bringing new information about the collaborations. We also show that such information is useful for social developer ranking, an actual task which is often part of different applications, such as team formation, community detection and pair programming. Finally, as software quality is intrinsic to the people who code it, our methodology and analyses represent initial steps towards people-centered software quality analysis, as further discussed throughout this article.

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Availability of data and material

Our dataset, called GitHub Socially Enhanced Dataset (GitSED), is publicly available in Zenodo (Oliveira et al., 2021).

Code availability

All relevant information related to this work is available in Project Apoena homepage: https://bit.ly/proj-apoena

Notes

  1. GitHub Mastering Issues: https://guides.github.com/features/issues

  2. Following GitHub documentation, repositories owned by user accounts have one owner, and ownership permissions cannot be shared with another user account. Owners may also invite users on GitHub to their repositories as collaborators.

  3. Following People: https://help.github.com/en/articles/following-people

  4. GitHub stars: https://help.github.com/en/articles/saving-repositories-with-stars

  5. The initial version of our research is published in Portuguese within a local venue (Rocha et al., 2016).

  6. TIOBE Index: https://www.tiobe.com/tiobe-index

  7. As of July 2021, most of such languages continue at the top 12, with exceptions of Ruby (17th), Perl (18th) and Pascal (20th). Their places in the top 12 are now filled with SQL (database language at 10th), Classic Visual Basic (at 11th), and R (environment-oriented at 12th). Hence, our analyses are still relevant for considering the most used coding languages.

  8. GitHub’s state of Octoverse: https://octoverse.github.com/2016/

  9. GHTorrent is an offline repository of data collected through the GitHub REST API.

  10. June 2019 is the most recent available dump at the time of defining our model. As of July 2021, there are only two versions newer than it: July 2020 and March 2021.

  11. Git Awards: https://github.com/vdaubry/github-awards

  12. The average node degree is calculated over all nodes in the network.

  13. The average CC is calculated over all nodes in the network.

  14. A graph is complete when all its nodes are connected by a unique edge.

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Funding

This work was supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Brazil.

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Authors and Affiliations

  1. Computer Science Department, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

    Gabriel P. Oliveira, Ana Flávia C. Moura, Natércia A. Batista, Andre Hora & Mirella M. Moro

  2. Instituto Federal de Minas Gerais, Ribeirão das Neves, Brazil

    Michele A. Brandão

Authors
  1. Gabriel P. Oliveira
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  2. Ana Flávia C. Moura
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  3. Natércia A. Batista
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Contributions

Gabriel P. Oliveira: conceptualization, methodology, software, formal analysis, investigation, writing — original draft, visualization. Ana Flávia C. Moura: software, investigation, data curation. Natércia A. Batista: conceptualization, methodology, investigation, writing — original draft. Michele A. Brandão: conceptualization, methodology, formal analysis, investigation, writing — original draft. Andre Hora: conceptualization, validation, writing — review and editing supervision. Mirella M. Moro: conceptualization, resources, writing — review and editing, supervision, project administration, funding acquisition.

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Correspondence to Mirella M. Moro.

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Oliveira, G.P., Moura, A.F.C., Batista, N.A. et al. How do developers collaborate? Investigating GitHub heterogeneous networks. Software Qual J 31, 211–241 (2023). https://doi.org/10.1007/s11219-022-09598-x

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