A dataset for pull-based development research

Georgios Gousios; Andy Zaidman

doi:10.1145/2597073.2597122

A dataset for pull-based development research

Software Engineering

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

41 Citations (Scopus)

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.

Original language	English
Title of host publication	11th Working Conference on Mining Software Repositories, MSR 2014 - Proceedings
Publisher	Association for Computing Machinery (ACM)
Pages	368-371
Number of pages	4
ISBN (Electronic)	9781450328630
DOIs	https://doi.org/10.1145/2597073.2597122
Publication status	Published - 31 May 2014
Event	11th International Working Conference on Mining Software Repositories, MSR 2014 - Hyderabad, India Duration: 31 May 2014 → 1 Jun 2014

Publication series

Name	11th Working Conference on Mining Software Repositories, MSR 2014 - Proceedings

Conference

Conference	11th International Working Conference on Mining Software Repositories, MSR 2014
Country/Territory	India
City	Hyderabad
Period	31/05/14 → 1/06/14

Keywords

Distributed software development
Empirical software engineering
Pull request
Pull-based development

Access to Document

10.1145/2597073.2597122

Cite this

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title = "A dataset for pull-based development research",

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.",

keywords = "Distributed software development, Empirical software engineering, Pull request, Pull-based development",

author = "Georgios Gousios and Andy Zaidman",

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booktitle = "11th Working Conference on Mining Software Repositories, MSR 2014 - Proceedings",

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note = "11th International Working Conference on Mining Software Repositories, MSR 2014 ; Conference date: 31-05-2014 Through 01-06-2014",

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Gousios, G & Zaidman, A 2014, A dataset for pull-based development research. in 11th Working Conference on Mining Software Repositories, MSR 2014 - Proceedings. 11th Working Conference on Mining Software Repositories, MSR 2014 - Proceedings, Association for Computing Machinery (ACM), pp. 368-371, 11th International Working Conference on Mining Software Repositories, MSR 2014, Hyderabad, India, 31/05/14. https://doi.org/10.1145/2597073.2597122

A dataset for pull-based development research. / Gousios, Georgios ; Zaidman, Andy.

11th Working Conference on Mining Software Repositories, MSR 2014 - Proceedings. Association for Computing Machinery (ACM), 2014. p. 368-371 (11th Working Conference on Mining Software Repositories, MSR 2014 - Proceedings).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AU - Zaidman, Andy

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KW - Empirical software engineering

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A dataset for pull-based development research

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Keywords

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