Workload-aware reviewer recommendation using a multi-objective search-based approach

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

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

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. We set out to empirically investigate the feasibility of automatically recommending reviewers while considering the review workload amongst other factors. 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. 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. Our empirical results demonstrate that the review workload and other important information should be taken into consideration in find-ing 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.

Original languageEnglish
Title of host publicationProceedings of the 16th ACM International Conference on Predictive Models and Data Analytics in Software Engineering
EditorsLeandro Minku, Tim Menzies, Mei Nagappan
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages21-30
Number of pages10
ISBN (Electronic)9781450381277
DOIs
Publication statusPublished - 2020
EventInternational Conference on Predictive Models and Data Analytics in Software Engineering 2020 - Virtual, United States of America
Duration: 5 Nov 20206 Nov 2020
Conference number: 16th
https://dl.acm.org/doi/proceedings/10.1145/3416508 (Proceedings)
https://promiseconf.github.io/2020/index.html (Website)

Conference

ConferenceInternational Conference on Predictive Models and Data Analytics in Software Engineering 2020
Abbreviated titlePROMISE 2020
CountryUnited States of America
CityVirtual
Period5/11/206/11/20
Internet address

Keywords

  • Code Review
  • Reviewer Recommendation
  • Search-Based Software Engineering

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