Recommending pull request reviewers based on code changes

Pull-based development supports collaborative distributed development. It enables developers to collaborate on projects hosted on GitHub. If a developer wants to collaborate on a project, he/she will fork the repository, make modifications on the forked repository and send a pull request to the development team to ask for a merge of the code changes to the official repository. When the development team receives a pull request, the team members will review the changes and make a decision on whether to accept the changes or not. However, efficiently finding suitable pull request reviewers is a challenge. In this paper, we propose a multi-instance-based deep neural network model to recommend reviewers for pull requests. Given a pull request, our model extracts three features, which pull request title, commit message, and code change. The proposed model extracts the three features automatically from the code changes of every commit in the pull request. The features of different commits are then merged to predict the likelihood that a reviewer candidate is the appropriate reviewer. We use CNN and LSTM-network to learn features since the pull requisition and commit message feature have different structures than code change, written in a programming language. To test the effectiveness of our model, we performed a set of experiments using 43,986 pull requests extracted from 12 open-source projects. We compare our model with two baselines approaches, CoreDevRec and Majority Classes. Experiments demonstrate that our model outperforms two state-of-the-art baselines. For instance, for the TensorFlow project, our model’s accuracy in determining the appropriate reviewers is 50.80%, 74.70%, and 84.04%, respectively, in Top-1, Top-3, and Top-5 recommendation.