{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T19:43:40Z","timestamp":1778701420042,"version":"3.51.4"},"reference-count":48,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,6,23]],"date-time":"2023-06-23T00:00:00Z","timestamp":1687478400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Software engineering is a comprehensive process that requires developers and team members to collaborate across multiple tasks. In software testing, bug triaging is a tedious and time-consuming process. Assigning bugs to the appropriate developers can save time and maintain their motivation. However, without knowledge about a bug\u2019s class, triaging is difficult. Motivated by this challenge, this paper focuses on the problem of assigning a suitable developer to a new bug by analyzing the history of developers\u2019 profiles and analyzing the history of bugs for all developers using machine learning-based recommender systems. Explainable AI (XAI) is AI that humans can understand. It contrasts with \u201cblack box\u201d AI, which even its designers cannot explain. By providing appropriate explanations for results, users can better comprehend the underlying insight behind the outcomes, boosting the recommender system\u2019s effectiveness, transparency, and confidence. The trained model is utilized in the recommendation stage to calculate relevance scores for developers based on expertise and past bug handling performance, ultimately presenting the developers with the highest scores as recommendations for new bugs. This approach aims to strike a balance between computational efficiency and accurate predictions, enabling efficient bug assignment while considering developer expertise and historical performance. In this paper, we propose two explainable models for recommendation. The first is an explainable recommender model for personalized developers generated from bug history to know what the preferred type of bug is for each developer. The second model is an explainable recommender model based on bugs to identify the most suitable developer for each bug from bug history.<\/jats:p>","DOI":"10.3390\/computers12070128","type":"journal-article","created":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T02:05:02Z","timestamp":1687745102000},"page":"128","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Improving Bug Assignment and Developer Allocation in Software Engineering through Interpretable Machine Learning Models"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-1727-7256","authenticated-orcid":false,"given":"Mina","family":"Samir","sequence":"first","affiliation":[{"name":"College of Computing and Information Technology, Arab Academy for Science, Technology & Maritime Transport, Alexandria 1029, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nada","family":"Sherief","sequence":"additional","affiliation":[{"name":"College of Computing and Information Technology, Arab Academy for Science, Technology & Maritime Transport, Alexandria 1029, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Walid","family":"Abdelmoez","sequence":"additional","affiliation":[{"name":"College of Computing and Information Technology, Arab Academy for Science, Technology & Maritime Transport, Alexandria 1029, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,23]]},"reference":[{"key":"ref_1","unstructured":"Roger, S. 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