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Methodol."],"published-print":{"date-parts":[[2024,11,30]]},"abstract":"<jats:p>\n            Modern Code Review (MCR) is an essential process in software development to ensure high-quality code. However, developers often spend considerable time reviewing code changes before being merged into the main code base. Previous studies attempted to predict whether a code change was going to be merged or abandoned soon after it was submitted to improve the code review process. However, these approaches require complex cost-sensitive learning, which makes their adoption challenging since it is difficult for developers to understand the main factors behind the models\u2019 predictions. To address this issue, we introduce in this article,\n            <jats:sc>MULTICR<\/jats:sc>\n            , a multi-objective search-based approach that uses Multi-Objective Genetic Programming (MOGP) to learn early code review prediction models as IF-THEN rules.\n            <jats:sc>MULTICR<\/jats:sc>\n            evolves predictive models while maximizing the accuracy of both merged and abandoned classes, eliminating the need for misclassification cost estimation. To evaluate MULTICR, we conducted an empirical study on 146,612 code reviews from Eclipse, LibreOffice, and Gerrithub. The obtained results show that\n            <jats:sc>MULTICR<\/jats:sc>\n            outperforms existing baselines in terms of Matthew Correlation Coefficient (MCC) and F1 scores while learning less complex models compared to decision trees. Our experiments also showed how\n            <jats:sc>MULTICR<\/jats:sc>\n            allows identifying the main factors related to abandoned code reviews as well as their associated thresholds, making it a promising approach for early code review prediction with notable performance and inter-operability. Additionally, we qualitatively evaluate\n            <jats:sc>MULTICR<\/jats:sc>\n            by conducting a user study through semi-structured interviews involving 10 practitioners from different organizations. The obtained results indicate that 90% of the participants find that\n            <jats:sc>MULTICR<\/jats:sc>\n            is useful and can help them to improve the code review process. Additionally, the learned IF-THEN rules of\n            <jats:sc>MULTICR<\/jats:sc>\n            are transparent.\n          <\/jats:p>","DOI":"10.1145\/3680472","type":"journal-article","created":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T15:00:38Z","timestamp":1722351638000},"page":"1-44","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["MULTICR: Predicting Merged and Abandoned Code Changes in Modern Code Review Using Multi-Objective Search"],"prefix":"10.1145","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1134-1324","authenticated-orcid":false,"given":"Moataz","family":"Chouchen","sequence":"first","affiliation":[{"name":"ETS Montreal, University of Quebec, Montreal, Quebec, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4708-0362","authenticated-orcid":false,"given":"Ali","family":"Ouni","sequence":"additional","affiliation":[{"name":"ETS Montreal, University of Quebec, Montreal, Quebec, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6010-7561","authenticated-orcid":false,"given":"Mohamed Wiem","family":"Mkaouer","sequence":"additional","affiliation":[{"name":"University of Michigan-Flint, Flint, MI, USA"}]}],"member":"320","published-online":{"date-parts":[[2024,12,3]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"MULTICR Replication Package___. 2024. 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