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A dataset for pull-based development research

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.

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Cited By

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  • (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
  • (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
  • (2024)Dependabot and security pull requests: large empirical studyEmpirical Software Engineering10.1007/s10664-024-10523-y29:5Online publication date: 30-Jul-2024
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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,
  • Program Chairs:
  • Sung Kim,
  • Martin Pinzger
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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  • 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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Author Tags

  1. distributed software development
  2. empirical software engineering
  3. pull request
  4. pull-based development

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Cited By

View all
  • (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
  • (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
  • (2024)Dependabot and security pull requests: large empirical studyEmpirical Software Engineering10.1007/s10664-024-10523-y29:5Online publication date: 30-Jul-2024
  • (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
  • (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
  • (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
  • (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
  • (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
  • (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
  • (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
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