Data analysis of correlation between project popularity and code change frequency

Dabeeruddin Syed, Jadran Sessa, Andreas Henschel, Davor Svetinovic

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Github is a source code management platform with social networking features that help increase the popularity of a project. The features of the GitHub like watch, star, fork and pull requests help make a project popular among the developers, in addition to enabling them to work on the code together. In this work, we study the relation between the project popularity and the continual code changes made to a GitHub project. The correlation is found by using the metrics such as the number of watchers, pull requests, and the number of commits. We correlate the time series of code change frequency with the time series of project popularity. As a result, we have found that projects with at least 1500 watchers each month have a strong positive correlation between the project popularity and frequency of code changes. We have also found that the number of pull requests is 73.2% more important to the popularity of a project than the number of watchers.

Original languageBritish English
Title of host publicationNeural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings
EditorsKazushi Ikeda, Minho Lee, Akira Hirose, Seiichi Ozawa, Kenji Doya, Derong Liu
PublisherSpringer Verlag
Pages36-43
Number of pages8
ISBN (Print)9783319466804
DOIs
StatePublished - 2016
Event23rd International Conference on Neural Information Processing, ICONIP 2016 - Kyoto, Japan
Duration: 16 Oct 201621 Oct 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9950 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd International Conference on Neural Information Processing, ICONIP 2016
Country/TerritoryJapan
CityKyoto
Period16/10/1621/10/16

Keywords

  • Data analytics
  • Mining software repositories
  • Open-source development

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