How diverse is your team? Investigating gender and nationality diversity in GitHub teams

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1 DIEE, University of Cagliari, Cagliari, Italy
2 Computer Science, Brunel University, London, UK
3 Department of Mathematics and Computer Science, University of Cagliari, Cagliari, Italy
DOI
10.7287/peerj.preprints.2285v1
Published
Accepted
Subject Areas
Data Mining and Machine Learning, Data Science, Natural Language and Speech, Social Computing, Software Engineering
Keywords
Affective Analysis, Issue Report, Empirical Software Engineering
Copyright
© 2016 Ortu et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Ortu M, Destefanis G, Counsell S, Swift S, Marchesi M, Tonelli R. 2016. How diverse is your team? Investigating gender and nationality diversity in GitHub teams. PeerJ Preprints 4:e2285v1

Abstract

Building an effective team of developers is a complex task faced by both software companies and open source communities. The problem of forming a “dream” team involves many variables, including consideration of human factors, and it is not a dilemma solvable in a mathematical way. Empirical studies might provide interesting insights to explain which factors need to be taken into account in building a team of developers and which levers act to optimise collaboration and productivity among developers. In this paper, we present the results of an empirical study aimed at investigating the link between team diver- sity (i.e., gender, nationality) and productivity (issue fixing time). We consider issues solved from the GHTorrent dataset inferring gender and nationality of each team’s members. We also evaluate the politeness of all comments involved in issue resolution. Results show that higher gender diversity is linked with a lower team average issue fixing time and that nationality diversity is linked with lower team politeness.

Author Comment

This is a preprint submission to PeerJ Preprints.