Code review is the process of inspecting code changes by a developer who is not involved in the development of the changeset. One of the initial and important steps of code review process is selecting code reviewer(s) for a given code change. To maximize the benefits of the code review process, the appropriate selection of the reviewer is essential. Code reviewer recommendation has been an active research area over the last few years, and many recommendation models have been proposed in the literature.

In this study, we conduct a systematic literature review by inspecting 29 primary studies published from 2009 to 2020. Based on the outcomes of our review: (1) most preferred approaches are heuristic approaches closely followed by machine learning approaches, (2) the majority of the studies use open source projects to evaluate their models, (3) the majority of the studies prefer incremental training set validation techniques, (4) most studies suffer from reproducibility problems, (5) model generalizability and dataset integrity are the most common validity threats for the models and (6) refining models and conducting additional experiments are the most common future work discussions in the studies.

代码审查是由不参与变更集开发的开发人员检查代码变更的过程。代码审查过程的初始且重要的步骤之一是为给定的代码更改选择代码审查者。为了最大限度地提高代码评审过程的好处，评论者的适当的选择是至关重要的。在过去的几年中，代码审查员推荐一直是活跃的研究领域，并且在文献中已经提出了许多推荐模型。

在这项研究中，我们对2009年至2020年发表的29项主要研究进行了系统的文献综述。根据我们的综述结果：（1）最优选的方法是启发式方法，紧随其后的是机器学习方法，（2）的研究使用开源项目来评估其模型，（3）大多数研究都喜欢采用增量训练集验证技术，（4）大多数研究都存在再现性问题，（5）模型的普遍性和数据集完整性是最常见的有效性这些模型的威胁以及（6）改进模型并进行其他实验是研究中最常见的未来工作讨论。