  Code review is an essential component of software development, playing a
vital role in ensuring a comprehensive check of code changes. However, the
continuous influx of pull requests and the limited pool of available reviewer
candidates pose a significant challenge to the review process, making the task
of assigning suitable reviewers to each review request increasingly difficult.
To tackle this issue, we present MIRRec, a novel code reviewer recommendation
method that leverages a hypergraph with multiplex relationships. MIRRec encodes
high-order correlations that go beyond traditional pairwise connections using
degree-free hyperedges among pull requests and developers. This way, it can
capture high-order implicit connectivity and identify potential reviewers. To
validate the effectiveness of MIRRec, we conducted experiments using a dataset
comprising 48,374 pull requests from ten popular open-source software projects
hosted on GitHub. The experiment results demonstrate that MIRRec, especially
without PR-Review Commenters relationship, outperforms existing stateof-the-art
code reviewer recommendation methods in terms of ACC and MRR, highlighting its
significance in improving the code review process.
