Automated Prediction of Bug Report Priority Using Multi-Factor Analysis

• Main Authors: TIAN, Yuan, LO, David, SUN, Chengnian, XIA, Xin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2015
• Subjects: Bug report management; Priority prediction; Multi-factor analysis; Computer Sciences; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/2437; https://ink.library.smu.edu.sg/context/sis_research/article/3437/viewcontent/AutomatedPredictionBugReportPriorityMulti_Factor_2015_afv.pdf
• Institution: Singapore Management University

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 %.