Title:	MANAGEMENT OF CODE REVIEWER RECOMMENDATION USING OPTIMIZATION ALGORITHM, NSGA-III
Author(s):	Zaeem Anwaar and Wasi Haider Butt
ISBN:	978-989-8704-48-1
Editors:	Miguel Baptista Nunes, Pedro Isaías and Philip Powell
Year:	2023
Edition:	Single
Keywords:	Code-Reviewer Recommendation, Modern Code Review, Software Development, Multi-Objective Algorithm, NSGA-III
Type:	Full Paper
First Page:	189
Last Page:	196
Language:	English
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Full Contents:	click to dowload
Paper Abstract:	Code review is considered as efficient and effective practice to improve software quality, identify and remove defects before integration. Code reviewers having right expertise, experience and apt amount of knowledge with the code being reviewed leads to successful code processes, fewer bugs and less maintenance cost. With the growing size of distributed development teams, picking suitable reviewers is a challenging task. However, due to less resources and shorter deadlines, the management of code reviews and appropriate recommendation of code reviewers based on three objectives consecutively is an ambitious task to be considered as aim of this research. This paper addresses the formulation for managing and recommending code reviewers based on multi conflicting objectives (i.e., availability, expertise and collaboration) simultaneously. Non-sorting genetic algorithm-III (NSGA-III) is used as optimization algorithm to find the most suitable reviewers while keeping expertise and availability ratio high and less collaboration between reviewers and developers. The results were implemented and validated on three (medium to large size) open-source projects named as LibreOffice, Qt and Open-Stack. We calculated precision, recall, mean reciprocal rank (MRR), accuracy for all 3 projects on average. The results from our proposed approach accurately recommended the code reviewers with the precision up to 80%, 86% of recall, 82% MRR and 84% accuracy by improving state-of-the-art. NSGA-III recommended the reviewers in less execution time and better fitness values in comparison to NSGA-II in all experimental sets. The proposed approach could be practical to Modern Code Review (MCR) in order to help developers while recommending suitable code-reviewers in less time and resources to speed up the review process.
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