CASTR: A Web-based Tool for Creating Bug Report Assignment Recommenders

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Devaiya, Disha Thakarshibhai
University of Lethbridge. Faculty of Arts and Science
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Lethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science
Large software development projects receive a large number of bug reports every day. Bug triage is a process where issues are screened and prioritized. Bug triage takes significant time and resources. For reducing the workload of project members, researchers have proposed using assignment recommenders. As the creation of bug report assignment recommenders is complex, we propose a web-based tool called the Creation Assistant for Supporting Triage Recommenders (CASTR) to assist the project members with the creation of assignment recommenders. CASTR assists a user in labeling and filtering the bug reports used for creating a project-specific assignment recommender. As the field study results present, recommenders can be created with good accuracy using CASTR such as 50-95% for Top-1 recommendations, 20-80% for Top-3 recommendations and 10-70% for Top-5 recommendations. Most participants (60%) found CASTR easy to use and were very likely to recommend CASTR for creating an assignment recommender.
Computer bugs , Computer software Development , Machine learning , Recommender systems (Information filtering)