Samir, M.; Sherief, N.; Abdelmoez, W. Improving Bug Assignment and Developer Allocation in Software Engineering through Interpretable Machine Learning Models. Computers2023, 12, 128.
Samir, M.; Sherief, N.; Abdelmoez, W. Improving Bug Assignment and Developer Allocation in Software Engineering through Interpretable Machine Learning Models. Computers 2023, 12, 128.
Samir, M.; Sherief, N.; Abdelmoez, W. Improving Bug Assignment and Developer Allocation in Software Engineering through Interpretable Machine Learning Models. Computers2023, 12, 128.
Samir, M.; Sherief, N.; Abdelmoez, W. Improving Bug Assignment and Developer Allocation in Software Engineering through Interpretable Machine Learning Models. Computers 2023, 12, 128.
Abstract
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's class, triaging is difficult. Motivated by this challenge, this paper focuses on the problem of assigning the suitable developer to new bug by analyzing the history of developers’ profiles and analyzing history of bugs for all developers using machine learning-based recommender systems. Explainable AI (XAI) is AI that humans can understand. It contrasts with "black box" AI, which even its designers can't explain. By providing appropriate explanations for results, users can better comprehend the underlying insight behind the outcomes, boosting the recommender system's effectiveness, transparency, and confidence. In this paper, we propose two explainable models for recommendation. The first one 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 generate the best developer for each bug from bug history.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
