@inproceedings {Lamkanfi2011CSMR,
	title = {Comparing Text Mining Algorithms for Predicting the Severity of a Reported Bug},
	journal = {Proceedings {CSMR}{\textquoteright}2011 (15th European Conference on Software Maintenance and Reengineering)},
	year = {2011},
	note = {[Acceptance ratio: 29/101 = 28.7.4\%]},
	publisher = {IEEE Press},
	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.},
	author = {Lamkanfi, Ahmed and Serge Demeyer and Soetens, Quinten David and Verdonck Tim}
}
