Comparing Mining Algorithms for Predicting the Severity of a Reported Bug

Authors:
Ahmed Lamkanfi;Serge Demeyer;Quinten David Soetens;Tim Verdonck
Affiliations:
-;-;-;-
Venue:
CSMR '11 Proceedings of the 2011 15th European Conference on Software Maintenance and Reengineering
Year:
2011

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Abstract

A critical item of a bug report is the so-called "severity", i.e. the impact the bug has on the successful execution of the software system. Consequently, tool support for the person reporting the bug in the form of a recommender or verification system is desirable. In previous work we made a first step towards such a tool: we demonstrated that text mining can predict the severity of a given bug report with a reasonable accuracy given a training set of sufficient size. In this paper we report on a follow-up study where we compare four well-known text mining algorithms (namely, Naive Bayes, Naive Bayes Multinomial, K-Nearest Neighbor and Support Vector Machines) with respect to accuracy and training set size. We discovered that for the cases under investigation (two open source systems: Eclipse and GNOME) Naive Bayes Multinomial performs superior compared to the other proposed algorithms.