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.

翻译：代码审查是软件开发中的重要环节，在确保代码变更的全面检查中起着关键作用。然而，持续涌入的拉取请求与有限的候选审查者资源给审查流程带来了显著挑战，使得为每个审查请求分配合适审查者的任务日益困难。针对这一问题，我们提出MIRRec——一种基于多重关系超图的新型代码审查者推荐方法。该方法通过构建包含拉取请求与开发者之间无度超边的结构，编码超越传统成对关联的高阶相关性，从而捕获高阶隐式连接并识别潜在审查者。为验证MIRRec的有效性，我们采用来自GitHub十个流行开源项目的48,374个拉取请求数据集进行实验。结果表明，MIRRec（尤其是剔除PR-审查评论者关系后）在ACC和MRR指标上均优于现有最优代码审查者推荐方法，凸显了其在改进代码审查流程中的重要意义。