Information Retrieval Based Nearest Neighbor Classification for Fine-Grained Bug Severity Prediction
Conference Proceeding Article
Bugs are prevalent in software systems. Some bugs are critical and need to be fixed right away, whereas others are minor and their fixes could be postponed until resources are available. In this work, we propose a new approach leveraging information retrieval, in particular BM25-based document similarity function, to automatically predict the severity of bug reports. Our approach automatically analyzes bug reports reported in the past along with their assigned severity labels, and recommends severity labels to newly reported bug reports. Duplicate bug reports are utilized to determine what bug report features, be it textual, ordinal, or categorical, are important. We focus on predicting fine-grained severity labels, namely the different severity labels of Bugzilla including: blocker, critical, major, minor, and trivial. Compared to the existing state-of-the-art study on fine-grained severity prediction, namely the work by Menzies and Marcus, our approach brings significant improvement.
Software and Cyber-Physical Systems
WCRE 2012: Proceedings of the 19th Working Conference on Reverse Engineering, 15-18 October 2012, Kingston, Ontario
IEEE Computer Society
City or Country
Los Alamitos, CA
TIAN, Yuan; LO, David; and SUN, Chengnian.
Information Retrieval Based Nearest Neighbor Classification for Fine-Grained Bug Severity Prediction. (2012). WCRE 2012: Proceedings of the 19th Working Conference on Reverse Engineering, 15-18 October 2012, Kingston, Ontario. 215-224. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1586