Context: Software code reviews are an important part of the development
process, leading to better software quality and reduced overall costs. However,
finding appropriate code reviewers is a complex and time-consuming task. Goals:
In this paper, we propose a large-scale study to compare performance of two
main source code reviewer recommendation algorithms (RevFinder and a Naive
Bayes-based approach) in identifying the best code reviewers for opened pull
requests. Method: We mined data from Github and Gerrit repositories, building a
large dataset of 51 projects, with more than 293K pull requests analyzed, 180K
owners and 157K reviewers. Results: Based on the large analysis, we can state
that i) no model can be generalized as best for all projects, ii) the usage of
a different repository (Gerrit, GitHub) can have impact on the the
recommendation results, iii) exploiting sub-projects information available in
Gerrit can improve the recommendation results.

本文通过在 Github 和 Gerrit 代码仓库中挖掘数据进行大规模实验比较两种主要的代码审查员推荐算法 (RevFinder 和基于朴素贝叶斯的方法) 的性能，以确定最佳的代码审查员。我们发现：1）没有一个模型适用于所有项目；2）不同的代码仓库会对推荐结果产生影响；3）利用 Gerrit 中可用的子项目信息可以改善推荐结果。