A Large-Scale Study on Source Code Reviewer Recommendation
Abstract
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.
Cite
@article{arxiv.1806.07619,
title = {A Large-Scale Study on Source Code Reviewer Recommendation},
author = {Jakub Lipcak and Bruno Rossi},
journal= {arXiv preprint arXiv:1806.07619},
year = {2018}
}
Comments
Published at the 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA 2018)