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DRONE: Predicting Priority of Reported Bugs by Multi-factor Analysis
29th IEEE International Conference on Software Maintenance (ICSM), 22-28 September 2013
  • Yuan TIAN, Singapore Management University
  • David LO, Singapore Management University
  • Chengnian SUN
Publication Type
Conference Proceeding Article
Publication Date
9-2013
Abstract

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 58.61%.

Identifier
10.1109/ICSM.2013.31
Publisher
IEEE
City or Country
Eindhoven
Additional URL
http://dx.doi.org/10.1109/ICSM.2013.31
Citation Information
Yuan TIAN, David LO and Chengnian SUN. "DRONE: Predicting Priority of Reported Bugs by Multi-factor Analysis" 29th IEEE International Conference on Software Maintenance (ICSM), 22-28 September 2013 (2013) p. 200 - 209 ISSN: 1063-6773
Available at: http://works.bepress.com/david_lo/134/