Paper Info
Title
Recommending peer reviewers in modern code review: a multi-objective search-based approach
Abstract
Modern code review is a common practice used by software developers to ensure high software quality in open source and industrial projects. During code review, developers submit their code changes which should be reviewed, via tool-based code review platforms, before being integrated into the codebase. Then, reviewers provide their feedback to developers, and may request further modifications before finally accepting or rejecting the submitted code changes. However, the identification of appropriate reviewers is still a tedious task as the number of code reviews to be performed is inflated with the increasing number of code changes and the increasing size of software development teams in today's large and active software projects. To help developers with the review process, we introduce a multi-objective search-based approach to find the appropriate set of reviewers. We use the Non-dominated Sorting Genetic Algorithm (NSGA-II) to optimize two conflicting objectives (i) maximize reviewers expertise with the changed files, and (ii) minimize reviewers workload in terms of their current open code reviews. We conduct a preliminary evaluation on two open source projects to evaluate our approach. Results indicate that our approach is efficient as compared to state-of-the-art approaches.
Year
DOI
Venue
2020
10.1145/3377929.3390057
GECCO '20: Genetic and Evolutionary Computation Conference Cancún Mexico July, 2020
DocType
ISBN
Citations 
Conference
978-1-4503-7127-8
0
PageRank 
References 
Authors
0.34
5
5
Name
Order
Citations
PageRank
Motaz Chouchen100.34
Ali Ouni2168.44
Mohamed Wiem Mkaouer322828.58
Raula Gaikovina Kula426425.82
Katsuro Inoue52424172.31