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Multi-objective code reviewer recommendations: balancing expertise, availability and collaborations

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Abstract

Modern Code review is one of the most critical tasks in software maintenance and evolution. A rigorous code review leads to fewer bugs and reduced overall maintenance costs. Most existing studies focus on automatically identifying the most qualified reviewers, based on their expertise, to review pull-up requests. However, the management of code reviews is a complex problem in practice due to a project’s limited resources, including the availability of peer reviewers. Furthermore, the history of collaborations between developers and reviewers could affect the quality of the reviews, in positive or negative ways. In this paper, we formulate the recommendation of code reviewers as a multi-objective search problem to balance the conflicting objectives of expertise, availability, and history of collaborations. Our validation confirms the effectiveness of our multi-objective approach on 9 open source projects by making better recommendations, on average, than the state of the art.

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Authors and Affiliations

  1. University of Michigan, Dearborn, USA

    Soumaya Rebai, Abderrahmen Amich, Somayeh Molaei & Marouane Kessentini

  2. University of Hawaii, Honolulu, USA

    Rick Kazman

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  1. Soumaya Rebai
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  2. Abderrahmen Amich
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  3. Somayeh Molaei
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  4. Marouane Kessentini
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  5. Rick Kazman
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Correspondence to Marouane Kessentini.

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Rebai, S., Amich, A., Molaei, S. et al. Multi-objective code reviewer recommendations: balancing expertise, availability and collaborations. Autom Softw Eng 27, 301–328 (2020). https://doi.org/10.1007/s10515-020-00275-6

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