A Large-Scale Study on Source Code Reviewer Recommendation

LIPČÁK, Jakub and Bruno ROSSI. A Large-Scale Study on Source Code Reviewer Recommendation. Online. In 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2018. Not specified: IEEE, 2018, p. 378-387. ISBN 978-1-5386-7383-6. Available from: https://dx.doi.org/10.1109/SEAA.2018.00068.

Basic information

• Original name: A Large-Scale Study on Source Code Reviewer Recommendation
• Authors: LIPČÁK, Jakub (703 Slovakia, belonging to the institution) and Bruno ROSSI (380 Italy, guarantor, belonging to the institution).
• Edition: Not specified, 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2018, p. 378-387, 10 pp. 2018.
• Publisher: IEEE

Other information

• Original language: English
• Type of outcome: Proceedings paper
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Country of publisher: United States of America
• Confidentiality degree: is not subject to a state or trade secret
• Publication form: electronic version available online
• WWW: URL
• RIV identification code: RIV/00216224:14610/18:00102742
• Organization unit: Institute of Computer Science
• ISBN: 978-1-5386-7383-6
• ISSN: 1089-6503
• Doi: http://dx.doi.org/10.1109/SEAA.2018.00068
• UT WoS: 000450238900059
• Keywords in English: Source Code Reviewer Recommendation; Distributed Software Development; Mining Software Repositories
• Tags: rivok
• Tags: International impact, Reviewed

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, 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.

Links

• EF16_013/0001802, research and development project: Name: CERIT Scientific Cloud
• LM2015085, research and development project: Name: CERIT Scientific Cloud (Acronym: CERIT-SC)

Investor: Ministry of Education, Youth and Sports of the CR, CERIT Scientific Cloud