Peer code review locates common coding rule violations and simple logical
errors in the early phases of software development, and thus reduces overall
cost. However, in GitHub, identifying an appropriate code reviewer for a pull
request is a non-trivial task given that reliable information for reviewer
identification is often not readily available. In this paper, we propose a code
reviewer recommendation technique that considers not only the relevant
cross-project work history (e.g., external library experience) but also the
experience of a developer in certain specialized technologies associated with a
pull request for determining her expertise as a potential code reviewer. We
first motivate our technique using an exploratory study with 10 commercial
projects and 10 associated libraries external to those projects. Experiments
using 17,115 pull requests from 10 commercial projects and six open source
projects show that our technique provides 85%--92% recommendation accuracy,
about 86% precision and 79%--81% recall in code reviewer recommendation, which
are highly promising. Comparison with the state-of-the-art technique also
validates the empirical findings and the superiority of our recommendation
technique.

本文提出了一种考虑到开发者特定技术专业性的代码审查者推荐技术，该技术利用外部库经验等关联历史，可提供高达 85%-92% 的准确性，86% 的精度和 79%-81% 的召回率，为代码审查提供高效帮助。

基于跨项目和技术经验的 GitHub 代码审查者推荐

CORRECT: Code Reviewer Recommendation in GitHub Based on Cross-Project  and Technology Experience

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。