Title

Information Retrieval Based Nearest Neighbor Classification for Fine-Grained Bug Severity Prediction

Publication Type

Conference Proceeding Article

Publication Date

10-2012

Abstract

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.

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

WCRE 2012: Proceedings of the 19th Working Conference on Reverse Engineering, 15-18 October 2012, Kingston, Ontario

First Page

215

Last Page

224

ISBN

9781467345361

Identifier

10.1109/WCRE.2012.31

Publisher

IEEE Computer Society

City or Country

Los Alamitos, CA

Additional URL

http://dx.doi.org/10.1109/WCRE.2012.31