Modern code review is a critical and indispensable practice in a pull-request development paradigm that prevails in Open Source Software (OSS) development. Finding a suitable reviewer in projects with massive participants thus becomes an increasingly challenging task. Many reviewer recommendation approaches (recommenders) have been developed to support this task which apply a similar strategy, i.e. modeling the review history first then followed by predicting/recommending a reviewer based on the model. Apparently, the better the model reflects the reality in review history, the higher recommender's performance we may expect. However, one typical scenario in a pull-request development paradigm, i.e. one Pull-Request (PR) (such as a revision or addition submitted by a contributor) may have multiple reviewers and they may impact each other through publicly posted comments, has not been modeled well in existing recommenders. We adopted the hypergraph technique to model this high-order relationship (i.e. one PR with multiple reviewers herein) and developed a new recommender, namely HGRec, which is evaluated by 12 OSS projects with more than 87K PRs, 680K comments in terms of accuracy and recommendation distribution. The results indicate that HGRec outperforms the state-of-the-art recommenders on recommendation accuracy. Besides, among the top three accurate recommenders, HGRec is more likely to recommend a diversity of reviewers, which can help to relieve the core reviewers' workload congestion issue. Moreover, since HGRec is based on hypergraph, which is a natural and interpretable representation to model review history, it is easy to accommodate more types of entities and realistic relationships in modern code review scenarios. As the first attempt, this study reveals the potentials of hypergraph on advancing the pragmatic solutions for code reviewer recommendation.
翻译：现代代码审查是开放源码软件(OSS)开发中流行的拉动要求发展范式中一个关键和不可或缺的做法。在有大批参与者的项目中找到合适的审查者,因此是一项越来越具有挑战性的任务。许多审查者建议方法(commenders)已经开发出来,以支持这项应用类似战略的任务,即先模拟审查历史,然后根据模型预测/建议审查者。显然,模型更好地反映审查历史中的现实情况,也就是我们可能期望的更高建议者的业绩。然而,在拉动要求发展范式中,一个典型的情景,即拉动要求(PR)(例如一个捐助方提交的修订或添加)可能具有多个审查者,它们可以通过公开发布的评论相互影响。我们采用了高层次技术来模拟这种基于模型的高度多样性关系(即一个拥有多个审查者的PR),并开发了一个新的建议者,即GGREC,由12个拥有超过87K PRc 、680K 精确度(PRP) (PR) (PR) (PR) (PR) (PR) (PLO) (PR) (PLO) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (其 ) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (
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