Article

A dataset for pull-based development research

Authors:

Georgios Gousios,

Andy ZaidmanAuthors Info & Claims

MSR 2014: Proceedings of the 11th Working Conference on Mining Software Repositories

Pages 368 - 371

https://doi.org/10.1145/2597073.2597122

Published: 31 May 2014 Publication History

Abstract

Pull requests form a new method for collaborating in distributed software development. To study the pull request distributed development model, we constructed a dataset of almost 900 projects and 350,000 pull requests, including some of the largest users of pull requests on Github. In this paper, we describe how the project selection was done, we analyze the selected features and present a machine learning tool set for the R statistics environment.

References

[1]

J. Anvik, L. Hiew, and G. C. Murphy. Who should fix this bug? In Proceedings of ICSE ’06, pages 361–370. ACM, 2006.

Digital Library

[2]

O. Baysal, R. Holmes, and M. W. Godfrey. Mining usage data and development artifacts. In Proceedings of MSR ’12, pages 98–107. IEEE.

[3]

C. Bird, A. Gourley, and P. Devanbu. Detecting patch submission and acceptance in oss projects. In Proceedings of MSR ’07, page 26. IEEE Computer Society, 2007.

Digital Library

[4]

C. Bird, A. Gourley, P. Devanbu, A. Swaminathan, and G. Hsu. Open borders? Immigration in open source projects. In Proceedings of MSR ’07, page 6. IEEE Computer Society, 2007.

Digital Library

[5]

L. Dabbish, C. Stuart, J. Tsay, and J. Herbsleb. Social coding in Github: transparency and collaboration in an open software repository. In Proceedings of CSCW ’12, pages 1277–1286. ACM, 2012.

Digital Library

[6]

E. Giger, M. Pinzger, and H. Gall. Predicting the fix time of bugs. In Proceedings of RSSE ’10, pages 52–56. ACM, 2010.

Digital Library

[7]

G. Gousios. The GHTorrent dataset and tool suite. In Proceedings of MSR ’13, pages 233–236. IEEE, 2013.

Digital Library

[8]

G. Gousios, M. Pinzger, and A. van Deursen. An exploration of the pull-based software development model. June 2014. To appear at ICSE 2014.

Digital Library

[9]

K. Hamasaki, R. G. Kula, N. Yoshida, A. E. C. Cruz, K. Fujiwara, and H. Iida. Who does what during a code review? datasets of oss peer review repositories. In Proceedings of MSR ’13, pages 49–52. IEEE, 2013.

Digital Library

[10]

G. Jeong, S. Kim, T. Zimmermann, and K. Yi. Improving code review by predicting reviewers and acceptance of patches. Research on Software Analysis for Error-free Computing Center Tech-Memo (ROSAEC MEMO), 2009.

[11]

M. Mukadam, C. Bird, and P. C. Rigby. Gerrit software code review data from Android. In Proceedings of MSR ’13, pages 45–48. IEEE, 2013.

Digital Library

[12]

N. Nagappan and T. Ball. Use of relative code churn measures to predict system defect density. In Proceedings of ICSE ’05, pages 284–292. ACM, 2005.

Digital Library

[13]

R. Pham, L. Singer, O. Liskin, F. Figueira Filho, and K. Schneider. Creating a shared understanding of testing culture on a social coding site. In Proceedings of ICSE ’13, pages 112–121. IEEE, 2013.

Digital Library

[14]

P. C. Rigby and C. Bird. Convergent software peer review practices. In Proceedings of FSE ’13, 2013.

Digital Library

[15]

P. Weißgerber, D. Neu, and S. Diehl. Small patches get in! In Proceedings of MSR ’08, pages 67–76. ACM, 2008.

Digital Library

Cited By

Schesch BFeatherman RYang KRoberts BErnst MFilkov VRay BZhou M(2024)Evaluation of Version Control Merge ToolsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695075(831-83)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695075
Franke LLiang HFarzanehpour SBrantly ADavis JBrown C(2024)An Exploratory Mixed-methods Study on General Data Protection Regulation (GDPR) Compliance in Open-Source SoftwareProceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement10.1145/3674805.3686692(325-336)Online publication date: 24-Oct-2024
https://dl.acm.org/doi/10.1145/3674805.3686692
Rebatchi HBissyandé TMoha N(2024)Dependabot and security pull requests: large empirical studyEmpirical Software Engineering10.1007/s10664-024-10523-y29:5Online publication date: 30-Jul-2024
https://dl.acm.org/doi/10.1007/s10664-024-10523-y
Show More Cited By

Index Terms

A dataset for pull-based development research
1. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Project and people management
      2. Software management
        Software maintenance
2. Software and its engineering
  1. Software creation and management
    1. Software development process management
    2. Software post-development issues
  2. Software notations and tools
    1. Software configuration management and version control systems

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Published In

cover image ACM Conferences

MSR 2014: Proceedings of the 11th Working Conference on Mining Software Repositories

May 2014

427 pages

ISBN:9781450328630

DOI:10.1145/2597073

General Chair:
Premkumar Devanbu
University of California at Davis, USA
,
Program Chairs:
Sung Kim
Hong Kong University of Science and Technology, China
,
Martin Pinzger
University of Klagenfurt, Austria

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSOFT: ACM Special Interest Group on Software Engineering

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TCSE: IEEE Computer Society's Tech. Council on Software Engin.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 May 2014

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ICSE '14

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SIGSOFT

ICSE '14: 36th International Conference on Software Engineering

May 31 - June 1, 2014

Hyderabad, India

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ICSE 2025

2025 IEEE/ACM 46th International Conference on Software Engineering

April 26 - May 3, 2025

Ottawa , ON , Canada

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Cited By

Schesch BFeatherman RYang KRoberts BErnst MFilkov VRay BZhou M(2024)Evaluation of Version Control Merge ToolsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695075(831-83)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695075
Franke LLiang HFarzanehpour SBrantly ADavis JBrown C(2024)An Exploratory Mixed-methods Study on General Data Protection Regulation (GDPR) Compliance in Open-Source SoftwareProceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement10.1145/3674805.3686692(325-336)Online publication date: 24-Oct-2024
https://dl.acm.org/doi/10.1145/3674805.3686692
Rebatchi HBissyandé TMoha N(2024)Dependabot and security pull requests: large empirical studyEmpirical Software Engineering10.1007/s10664-024-10523-y29:5Online publication date: 30-Jul-2024
https://dl.acm.org/doi/10.1007/s10664-024-10523-y
Khatami AZaidman A(2024)State‐of‐the‐practice in quality assurance in Java‐based open source software developmentSoftware: Practice and Experience10.1002/spe.332154:8(1408-1446)Online publication date: 4-Mar-2024
https://doi.org/10.1002/spe.3321
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
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
Zhang XYu YGousios GRastogi A(2023)Pull Request Decisions Explained: An Empirical OverviewIEEE Transactions on Software Engineering10.1109/TSE.2022.316505649:2(849-871)Online publication date: 1-Feb-2023
https://doi.org/10.1109/TSE.2022.3165056
Khatami AZaidman A(2023)Quality Assurance Awareness in Open Source Software Projects on GitHub2023 IEEE 23rd International Working Conference on Source Code Analysis and Manipulation (SCAM)10.1109/SCAM59687.2023.00027(174-185)Online publication date: 2-Oct-2023
https://doi.org/10.1109/SCAM59687.2023.00027
Manasa Venigalla AChimalakonda S(2023)DocMine: A Software Documentation-Related Dataset of 950 GitHub Repositories2023 IEEE/ACM 20th International Conference on Mining Software Repositories (MSR)10.1109/MSR59073.2023.00062(407-411)Online publication date: May-2023
https://doi.org/10.1109/MSR59073.2023.00062
Reich PMaalei W(2023)Testability Refactoring in Pull Requests: Patterns and Trends2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)10.1109/ICSE48619.2023.00131(1508-1519)Online publication date: May-2023
https://doi.org/10.1109/ICSE48619.2023.00131
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