Authors: Yuan Tian David Lo Chengnian Sun
Venue: ICSME 2013 IEEE International Conference on Software Maintenance, pp. 200-209, 2013
Year: 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%.
BibTeX:
@inproceedings{yuantian2013dpporbbma,
author = "Yuan Tian and David Lo and Chengnian Sun",
title = "DRONE: Predicting Priority of Reported Bugs by Multi-factor Analysis",
year = "2013",
pages = "200-209",
booktitle = "Proceedings of 2013 IEEE International Conference on Software Maintenance"
}
Plain Text:
Yuan Tian, David Lo, and Chengnian Sun, "DRONE: Predicting Priority of Reported Bugs by Multi-factor Analysis," 2013 IEEE International Conference on Software Maintenance, pp. 200-209