Publication Type
Journal Article
Version
acceptedVersion
Publication Date
6-2021
Abstract
The continuous contributions made by long time contributors (LTCs) are a key factor enabling open source software (OSS) projects to be successful and survival. We study Github as it has a large number of OSS projects and millions of contributors, which enables the study of the transition from newcomers to LTCs. In this paper, we investigate whether we can effectively predict newcomers in OSS projects to be LTCs based on their activity data that is collected from Github. We collect Github data from GHTorrent, a mirror of Github data. We select the most popular 917 projects, which contain 75,046 contributors. We determine a developer as a LTC of a project if the time interval between his/her first and last commit in the project is larger than a certain time TT. In our experiment, we use three different settings on the time interval: 1, 2, and 3 years. There are 9,238, 3,968, and 1,577 contributors who become LTCs of a project in three settings of time interval, respectively. To build a prediction model, we extract many features from the activities of developers on Github, which group into five dimensions: developer profile, repository profile, developer monthly activity, repository monthly activity, and collaboration network. We apply several classifiers including naive Bayes, SVM, decision tree, kNN and random forest. We find that random forest classifier achieves the best performance with AUCs of more than 0.75 in all three settings of time interval for LTCs. We also investigate the most important features that differentiate newcomers who become LTCs from newcomers who stay in the projects for a short time. We find that the number of followers is the most important feature in all three settings of the time interval studied. We also find that the programming language and the average number of commits contributed by other developers when a newcomer joins a project also belong to the top 10 most important features in all three settings of time interval for LTCs. Finally, we provide several implications for action based on our analysis results to help OSS projects retain newcomers.
Keywords
Long Time Contributor, GitHub, Prediction Model, Feature extraction, Task analysis
Discipline
Numerical Analysis and Scientific Computing | Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
IEEE Transactions on Software Engineering
Volume
47
Issue
6
First Page
1277
Last Page
1298
ISSN
0098-5589
Identifier
10.1109/TSE.2019.2918536
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
BAO, Lingfeng; XIA, Xin; LO, David; and MURPHY, Gail C..
A large scale study of long-time contributor prediction for GitHub projects. (2021). IEEE Transactions on Software Engineering. 47, (6), 1277-1298.
Available at: https://ink.library.smu.edu.sg/sis_research/4359
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/TSE.2019.2918536