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

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dc.contributor.authorDevaiya, Disha Thakarshibhai
dc.contributor.authorUniversity of Lethbridge. Faculty of Arts and Science
dc.contributor.supervisorAnvik, John
dc.date.accessioned2019-08-27T17:49:21Z
dc.date.available2019-08-27T17:49:21Z
dc.date.issued2019
dc.degree.levelMastersen_US
dc.description.abstractLarge 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.en_US
dc.description.sponsorshipNatural Sciences and Engineering Research Council (NSERC)en_US
dc.identifier.urihttps://hdl.handle.net/10133/5520
dc.language.isoen_USen_US
dc.proquest.subjectComputer science [0984]en_US
dc.proquest.subjectComputer engineering [0464]en_US
dc.proquestyesYesen_US
dc.publisherLethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Scienceen_US
dc.publisher.departmentDepartment of Mathematics and Computer Scienceen_US
dc.publisher.facultyArts and Scienceen_US
dc.relation.ispartofseriesThesis (University of Lethbridge. Faculty of Arts and Science)en_US
dc.subjectComputer bugsen_US
dc.subjectComputer software Developmenten_US
dc.subjectMachine learningen_US
dc.subjectRecommender systems (Information filtering)en_US
dc.titleCASTR: A Web-based Tool for Creating Bug Report Assignment Recommendersen_US
dc.typeThesisen_US
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