Bugs are prevalent. To improve software quality, developers often allow users to report bugs that they found using a bug tracking system such as Bugzilla. Users would specify among other things, a description of the bug, the component that is affected by the bug, and the severity of the bug. Based on this information, bug triagers would then assign a priority level to the reported bug. As resources are limited, bug reports would be investigated based on their priority levels. This priority assignment process however is a manual one. Could we do better? In this paper, we propose an automated approach based on machine learning that would recommend a priority level based on information available in bug reports. Our approach considers multiple factors, temporal, textual, author, related-report, severity, and product, that potentially affect the priority level of a bug report. These factors are extracted as features which are then used to train a discriminative model via a new classification algorithm that handles ordinal class labels and imbalanced data. Experiments on more than a hundred thousands bug reports from Eclipse show that we can outperform baseline approaches in terms of average F-measure by a relative improvement of up to 209 %.
Bug report management, Priority prediction, Multi-factor analysis
Computer Sciences | Software Engineering
Software and Cyber-Physical Systems
Empirical Software Engineering
TIAN, Yuan; LO, David; SUN, Chengnian; and XIA, Xin.
Automated Prediction of Bug Report Priority Using Multi-Factor Analysis. (2015). Empirical Software Engineering. 20, (5), 1354-1383. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2437
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