A Large-Scale Study on Source Code Reviewer Recommendation
| Authors | |
|---|---|
| Year of publication | 2018 |
| Type | Article in Proceedings |
| Conference | 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2018 |
| MU Faculty or unit | |
| Citation | |
| web | https://ieeexplore.ieee.org/document/8498235 |
| Doi | http://dx.doi.org/10.1109/SEAA.2018.00068 |
| Keywords | Source Code Reviewer Recommendation; Distributed Software Development; Mining Software Repositories |
| Description | 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, Naive Bayes-based) 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 different repository (Gerrit, GitHub) has a large impact on the the recommendation results, iii) exploiting sub-projects information available in Gerrit improves the recommendation results. |
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