A Large-Scale Study on Source Code Reviewer Recommendation

Publikace nespadá pod Filozofickou fakultu, ale pod Ústav výpočetní techniky. Oficiální stránka publikace je na webu muni.cz.



Druh Článek ve sborníku
Konference 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2018
Fakulta / Pracoviště MU

Ústav výpočetní techniky

www https://ieeexplore.ieee.org/document/8498235
Doi http://dx.doi.org/10.1109/SEAA.2018.00068
Klíčová slova Source Code Reviewer Recommendation; Distributed Software Development; Mining Software Repositories
Popis 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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