A First Look at Fairness of Machine Learning Based Code Reviewer Recommendation

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dc.contributor.advisorWang, Song
dc.contributor.authorMohajer, Mohammad Mahdi
dc.date.accessioned2024-07-18T21:19:53Z
dc.date.available2024-07-18T21:19:53Z
dc.date.copyright2024-04-10
dc.date.issued2024-07-18
dc.date.updated2024-07-18T21:19:53Z
dc.degree.disciplineElectrical and Computer Engineering
dc.degree.levelMaster's
dc.degree.nameMASc - Master of Applied Science
dc.description.abstractThe fairness of machine learning (ML) approaches is critical to the reliability of modern artificial intelligence systems. Despite extensive study on this topic, the fairness of ML models in the software engineering (SE) domain has yet to be well explored. As a result, many ML-powered software systems, particularly those utilized in the software engineering community, continue to be prone to fairness issues. Taking one of the typical SE tasks, i.e., code reviewer recommendation, as a subject, this work conducts the first study toward investigating the fairness of ML applications in the SE domain, explicitly focusing on the code reviewer recommendation task. Our empirical study demonstrates that current state-of-the-art ML-based code reviewer recommendation techniques exhibit unfairness and discriminating behaviors. Specifically, male reviewers get, on average, 7.25% more recommendations than female code reviewers compared to their distribution in the reviewer set. This work also discusses why the studied ML-based code reviewer recommendation systems are unfair and provides solutions to mitigate the unfairness. For instance, these techniques may recommend male reviewers at a significantly higher rate than female reviewers in a discriminatory manner. Our study further indicates that existing mitigation methods can significantly enhance fairness in projects with a similar distribution of protected and privileged groups. Still, their effectiveness in improving fairness on imbalanced or skewed data is limited. Eventually, we suggest a solution to overcome the drawbacks of existing mitigation techniques and tackle bias in imbalanced or skewed datasets.
dc.identifier.urihttps://hdl.handle.net/10315/42143
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectComputer science
dc.subjectComputer engineering
dc.subject.keywordsFairness analysis
dc.subject.keywordsMachine learning
dc.subject.keywordsCode reviewer recommendation
dc.titleA First Look at Fairness of Machine Learning Based Code Reviewer Recommendation
dc.typeElectronic Thesis or Dissertation

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