The pull-based development process has become prevalent on platforms such as GitHub as a form of distributed software development. Potential contributors can create and submit a set of changes to a software project through pull requests. These changes can be accepted, discussed or rejected by the maintainers of the software project, and can influence further contribution proposals. As such, it is important to examine the practices that encourage contributors to a project to submit pull requests. Specifically, we consider the impact of prior pull requests on the acceptance or rejection of subsequent pull requests. We also consider the potential effect of rejecting or ignoring pull requests on further contributions. In this preliminary research, we study three large projects on GitHub , using pull request data obtained through the GitHub API, and we perform empirical analyses to investigate the above questions. Our results show that continued contribution to a project is correlated with higher pull request acceptance rates and that pull request rejections lead to fewer future contributions.
@cite_7 studied the factors that contribute to latency in PR reviews, defining this latency as the time interval between pull request creation and closing date''. They found that PR latency is mainly affected by process-related factors such as whether a PR was assigned to a specific reviewer or not. They also found that continuous integration is a dominant factor in PR latency.
@cite_7
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The pull-based development model, enabled by git and popularised by collaborative coding platforms like Bit Bucket, 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.