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Wisam Haitham Abbood Al-Zubaidi, Patanamon Thongtanunam, Hoa Khanh Dam, Chakkrit Tantithamthavorn, Aditya Ghose

The 16th ACM SIGSOFT International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE2020)

Background: Reviewer recommendation approaches have been proposed to provide automated support in finding suitable reviewers to review a given patch. However, they mainly focused on reviewer experience, and did not take into account the review workload, which is another important factor for a reviewer to decide if they will accept a review invitation. Aim: We set out to empirically investigate the feasibility of automatically recommending reviewers while considering the review workload amongst other factors. Method: We develop a novel approach that leverages a multi-objective meta-heuristic algorithm to search for reviewers guided by two objectives , i.e., (1) maximizing the chance of participating in a review, and (2) minimizing the skewness of the review workload distribution among reviewers. Results: Through an empirical study of 230,090 patches with 7,431 reviewers spread across four open source projects, we find that our approach can recommend reviewers who are potentially suitable for a newly-submitted patch with 19%-260% higher F-measure than the five benchmarks. Conclusion: Our empirical results demonstrate that the review workload and other important information should be taken into consideration in finding reviewers who are potentially suitable for a newly-submitted patch. In addition, the results show the effectiveness of realizing this approach using a multi-objective search-based approach.

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@inproceedings{Al-ZubaidiPROMISE2020,
	Author={Al-Zubaidi, Wisam Haitham Abbood  and Thongtanunam, Patanamon and Dam, Hoa Khanh and Tantithamthavorn, Chakkrit and Ghose, Aditya},
	Title = {Workload-Aware Reviewer Recommendation using a Multi-objective Search-Based Approach},
	Booktitle = {Proceedings of the 16th ACM SIGSOFT International Conference on Predictive Models and Data Analytics in Software Engineering},
	Pages = {to appear},
	Year = {2020},
	doi = {10.1145/3416508.3417115}
}