Abstract
Code review is an important part of the software development process. Recently, many open source projects have begun practicing code review through "modern" tools such as GitHub pull-requests and Gerrit. Many commercial software companies use similar tools for code review internally. These tools enable the owner of a source code change to request individuals to participate in the review, i.e., reviewers. However, this task comes with a challenge. Prior work has shown that the benefits of code review are dependent upon the expertise of the reviewers involved. Thus, a common problem faced by authors of source code changes is that of identifying the best reviewers for their source code change. To address this problem, we present an approach, namely cHRev, to automatically recommend reviewers who are best suited to participate in a given review, based on their historical contributions as demonstrated in their prior reviews. We evaluate the effectiveness of cHRev on three open source systems as well as a commercial codebase at Microsoft and compare it to the state of the art in reviewer recommendation. We show that by leveraging the specific information in previously completed reviews (i.e.,quantification of review comments and their recency), we are able to improve dramatically on the performance of prior approaches, which (limitedly) operate on generic review information (i.e., reviewers of similar source code file and path names) or source coderepository data. We also present the insights into why our approach cHRev outperforms the existing approaches.