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

Authors: Yuan Tian David Lo Chengnian Sun

Venue: SANER   2012 19th Working Conference on Reverse Engineering, pp. 215-224, 2012

Year: 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.

BibTeX:

@inproceedings{yuantian2012irbnncffbsp,
    author = "Yuan Tian and David Lo and Chengnian Sun",
    title = "Information Retrieval Based Nearest Neighbor Classification for Fine-Grained Bug Severity Prediction",
    year = "2012",
    pages = "215-224",
    booktitle = "Proceedings of 2012 19th Working Conference on Reverse Engineering"
}

Plain Text:

Yuan Tian, David Lo, and Chengnian Sun, "Information Retrieval Based Nearest Neighbor Classification for Fine-Grained Bug Severity Prediction," 2012 19th Working Conference on Reverse Engineering, pp. 215-224