Cross project Reopened Pull Request Prediction in GitHub
In GitHub, pull requests may get reopened again for further modification and code review. Prediction of within project reopened pull requests work well if there is enough amount of training data to build the training model. However, for new projects that have a limited amount of pull requests, using training data from other projects can help to predict the reopened pull requests. Therefore, it is important to study cross project reopened pull request prediction and help integrators in new projects. In this paper, we propose a cross project approach that consists of building a decision tree training model based on an external project as a source project to predict the reopened pull requests in another project. We evaluate the effectiveness of cross project prediction on 7 open source projects containing 100,622 pull requests. Experiment results show that the cross project prediction achieves accuracy from 78.76% to 96.52% and F1 measure from 53.34% to 90.58% across 7 projects. We examine the feature importance using the decision tree predictor and find that the number of commits is the most important feature in the majority of projects. Keywords Reopened pull request prediction, Cross project, GitHub.
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