Abstract
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.
