Abstract | ||
---|---|---|
The pull-based development model, enabled by git and popularised by collaborative coding platforms like BitBucket, Gitorius, and GitHub, is widely used in distributed software teams. While this model lowers the barrier to entry for potential contributors (since anyone can submit pull requests to any repository), it also increases the burden on integrators (i.e., members of a project's core team, responsible for evaluating the proposed changes and integrating them into the main development line), who struggle to keep up with the volume of incoming pull requests. In this paper we report on a quantitative study that tries to resolve which factors affect pull request evaluation latency in GitHub. Using regression modeling on data extracted from a sample of GitHub projects using the Travis-CI continuous integration service, we find that latency is a complex issue, requiring many independent variables to explain adequately.
|
Year | DOI | Venue |
---|---|---|
2015 | 10.1109/MSR.2015.42 | MSR |
Keywords | Field | DocType |
pull-request evaluation latency,GitHub,pull-based development model,collaborative coding platforms,BitBucket,Gitorius,GitHub,distributed software teams,project core team,main development line,incoming pull requests,quantitative study,regression modeling,Travis-CI continuous integration service | Data mining,Computer science,Latency (engineering),Coding (social sciences),Systems development life cycle,Bit bucket,Software construction,Software verification and validation,Software quality,Operating system,Process mining | Conference |
Volume | ISSN | ISBN |
2 | 2160-1852 | 978-0-7695-5594-2 |
Citations | PageRank | References |
39 | 1.22 | 25 |
Authors | ||
5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Yue Yu | 1 | 219 | 29.56 |
Wang Huaimin | 2 | 1025 | 121.31 |
Vladimir Filkov | 3 | 1503 | 75.32 |
Premkumar Devanbu | 4 | 4956 | 357.68 |
Bogdan Vasilescu | 5 | 935 | 48.75 |