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Predicting software revision outcomes on GitHub using structural holes theory

Predicting software revision outcomes on GitHub using structural holes theory
Predicting software revision outcomes on GitHub using structural holes theory
Many software repositories are hosted publicly online via social platforms. Online users contribute to the software projects not only by providing feedback and suggestions, but also by submitting revisions to improve the software quality. This study takes a close look at revisions and examines the impact of social media networks on the revision outcome. A novel approach with a mix of different research methods (e.g., ego-centric social network analysis, structural holes theory and survival analysis) is used to build a comprehensible model to predict the revision outcome. The predictive performance is validated using real life datasets obtained from GitHub, the social coding website, which contains 32,962 pull requests to submit revisions, 20,399 distinctive software project repositories, and a social network of 234,322 users. Good predictive performance has been achieved with an average AUC of 0.84. The results suggest that a repository host's position in the ego network plays an important role in determining the duration before a revision is accepted. Specifically, hosts that are positioned in between densely connected social groups are likely to respond more quickly to accept the revisions. The study demonstrates that online social networks are vital to software development and advances the understanding of collaboration in software development research. The proposed method can be applied to support decision making in software development to forecast revision duration. The result also has several implications for managing project collaboration using social media.
1389-1286
114-124
Li, Libo
838dda30-da62-41ad-b57c-bee6ad59acd3
Goethals, Frank
a25dee80-a572-4310-90a5-9ea8cdc2fc5f
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Snoeck, Monique
9aee96bc-8a57-4c37-bcd7-e83f0b173ee1
Li, Libo
838dda30-da62-41ad-b57c-bee6ad59acd3
Goethals, Frank
a25dee80-a572-4310-90a5-9ea8cdc2fc5f
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Snoeck, Monique
9aee96bc-8a57-4c37-bcd7-e83f0b173ee1

Li, Libo, Goethals, Frank, Baesens, Bart and Snoeck, Monique (2017) Predicting software revision outcomes on GitHub using structural holes theory. Computer Networks, 114, 114-124. (doi:10.1016/j.comnet.2016.08.024).

Record type: Article

Abstract

Many software repositories are hosted publicly online via social platforms. Online users contribute to the software projects not only by providing feedback and suggestions, but also by submitting revisions to improve the software quality. This study takes a close look at revisions and examines the impact of social media networks on the revision outcome. A novel approach with a mix of different research methods (e.g., ego-centric social network analysis, structural holes theory and survival analysis) is used to build a comprehensible model to predict the revision outcome. The predictive performance is validated using real life datasets obtained from GitHub, the social coding website, which contains 32,962 pull requests to submit revisions, 20,399 distinctive software project repositories, and a social network of 234,322 users. Good predictive performance has been achieved with an average AUC of 0.84. The results suggest that a repository host's position in the ego network plays an important role in determining the duration before a revision is accepted. Specifically, hosts that are positioned in between densely connected social groups are likely to respond more quickly to accept the revisions. The study demonstrates that online social networks are vital to software development and advances the understanding of collaboration in software development research. The proposed method can be applied to support decision making in software development to forecast revision duration. The result also has several implications for managing project collaboration using social media.

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Predicting software revision outcomes on GitHub using structural holes theory - Accepted Manuscript
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More information

Accepted/In Press date: 24 August 2016
e-pub ahead of print date: 27 August 2016
Published date: 26 February 2017

Identifiers

Local EPrints ID: 425619
URI: http://eprints.soton.ac.uk/id/eprint/425619
ISSN: 1389-1286
PURE UUID: cc8a6138-bb6f-45f4-b24f-645d4430f0ce
ORCID for Libo Li: ORCID iD orcid.org/0000-0003-1658-5157
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

Catalogue record

Date deposited: 26 Oct 2018 16:30
Last modified: 28 Apr 2022 02:29

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Contributors

Author: Libo Li ORCID iD
Author: Frank Goethals
Author: Bart Baesens ORCID iD
Author: Monique Snoeck

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