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Do biases related to geographical location influence work-related decisions in GitHub?

Author:

Ayushi RastogiAuthors Info & Claims

ICSE '16: Proceedings of the 38th International Conference on Software Engineering Companion

Pages 665 - 667

https://doi.org/10.1145/2889160.2891035

Published: 14 May 2016 Publication History

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Abstract

Visible demographic characteristics are seen as elements of bias in offline work environments. In this study, we investigate the influence of the geographical location on the evaluation of pull requests in GitHub -- the most popular online collaborative code development environment. We use a mixed-methods approach and present analyses of 70,000+ pull requests and 2,500+ survey responses. Quantitative analysis of GitHub projects' data suggests that the geographical location significantly explains the pull request acceptance decisions. These observations are in agreement with the perceptions of submitters based on their experiences with bias. Integrators feel that it is easy to work with contributors from the same geographical location and that they encourage contributors from the same geographical location. However, integrators do not feel that contributors from some countries are better at writing pull requests compared to others.

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Cited By

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Khatoonabadi SAbdellatif ACosta DShihab E(2024)Predicting the First Response Latency of Maintainers and Contributors in Pull RequestsIEEE Transactions on Software Engineering10.1109/TSE.2024.344374150:10(2529-2543)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1109/TSE.2024.3443741
Khatoonabadi SCosta DMujahid SShihab E(2023)Understanding the Helpfulness of Stale Bot for Pull-Based Development: An Empirical Study of 20 Large Open-Source ProjectsACM Transactions on Software Engineering and Methodology10.1145/362473933:2(1-43)Online publication date: 23-Dec-2023
https://dl.acm.org/doi/10.1145/3624739
Fischer FHöbenreich JGrossklags JMeng WJensen CCremers CKirda E(2023)The Effectiveness of Security Interventions on GitHubProceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security10.1145/3576915.3623174(2426-2440)Online publication date: 15-Nov-2023
https://dl.acm.org/doi/10.1145/3576915.3623174
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Index Terms

Do biases related to geographical location influence work-related decisions in GitHub?
1. Software and its engineering
  1. Software creation and management
    1. Collaboration in software development
      1. Programming teams

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Information & Contributors

Information

Published In

ICSE '16: Proceedings of the 38th International Conference on Software Engineering Companion

May 2016

946 pages

ISBN:9781450342056

DOI:10.1145/2889160

General Chair:
Laura Dillon
Michigan State University
,
Program Chairs:
Willem Visser
Stellenbosch University, South Africa
,
Laurie Williams
North Carolina State University

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 May 2016

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ICSE '16: 38th International Conference on Software Engineering

May 14 - 22, 2016

Texas, Austin

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Overall Acceptance Rate 276 of 1,856 submissions, 15%

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Cited By

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Khatoonabadi SAbdellatif ACosta DShihab E(2024)Predicting the First Response Latency of Maintainers and Contributors in Pull RequestsIEEE Transactions on Software Engineering10.1109/TSE.2024.344374150:10(2529-2543)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1109/TSE.2024.3443741
Khatoonabadi SCosta DMujahid SShihab E(2023)Understanding the Helpfulness of Stale Bot for Pull-Based Development: An Empirical Study of 20 Large Open-Source ProjectsACM Transactions on Software Engineering and Methodology10.1145/362473933:2(1-43)Online publication date: 23-Dec-2023
https://dl.acm.org/doi/10.1145/3624739
Fischer FHöbenreich JGrossklags JMeng WJensen CCremers CKirda E(2023)The Effectiveness of Security Interventions on GitHubProceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security10.1145/3576915.3623174(2426-2440)Online publication date: 15-Nov-2023
https://dl.acm.org/doi/10.1145/3576915.3623174
Khatoonabadi SCosta DAbdalkareem RShihab E(2023)On Wasted Contributions: Understanding the Dynamics of Contributor-Abandoned Pull Requests–A Mixed-Methods Study of 10 Large Open-Source ProjectsACM Transactions on Software Engineering and Methodology10.1145/353078532:1(1-39)Online publication date: 13-Feb-2023
https://dl.acm.org/doi/10.1145/3530785
Weeraddana NXu XAlfadel MMcIntosh SNagappan M(2023)An empirical comparison of ethnic and gender diversity of DevOps and non-DevOps contributions to open-source projectsEmpirical Software Engineering10.1007/s10664-023-10394-928:6Online publication date: 10-Nov-2023
https://doi.org/10.1007/s10664-023-10394-9
Rossi DZacchiroli SLo DMcIntosh SNovielli N(2022)Geographic diversity in public code contributionsProceedings of the 19th International Conference on Mining Software Repositories10.1145/3524842.3528471(80-85)Online publication date: 23-May-2022
https://dl.acm.org/doi/10.1145/3524842.3528471
Rossi DZacchiroli SBegel ABlincoe K(2022)Worldwide gender differences in public code contributionsProceedings of the 2022 ACM/IEEE 44th International Conference on Software Engineering: Software Engineering in Society10.1145/3510458.3513011(172-183)Online publication date: 21-May-2022
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Li ZYu YWang TYin GLi SWang H(2022)Are You Still Working on This? An Empirical Study on Pull Request AbandonmentIEEE Transactions on Software Engineering10.1109/TSE.2021.305340348:6(2173-2188)Online publication date: 1-Jun-2022
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Rossi DZacchiroli S(2022)Worldwide Gender Differences in Public Code Contributions and how they have been affected by the COVID-19 pandemic2022 IEEE/ACM 44th International Conference on Software Engineering: Software Engineering in Society (ICSE-SEIS)10.1109/ICSE-SEIS55304.2022.9794118(172-183)Online publication date: May-2022
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Almarimi NOuni AChouchen MMkaouer M(2022)Improving the detection of community smells through socio‐technical and sentiment analysisJournal of Software: Evolution and Process10.1002/smr.250535:6Online publication date: 2-Sep-2022
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