@conference {LamkanfiCSMR2011,
	title = {Comparing Text Mining Algorithms for Predicting the Severity of a Reported Bug},
	booktitle = {CSMR 2011: The 15th European Conference on Software Maintenance and Reengineering},
	year = {2011},
	abstract = {A critical item of a bug report is the so-called {\textquotedblleft}severity{\textquotedblright}, 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, Na ̈{\i}ve Bayes, Na ̈{\i}ve 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) Na ̈{\i}ve Bayes Multinomial performs superior compared to the other proposed algorithms.},
	author = {Lamkanfi, Ahmed and Serge Demeyer and Soetens, Quinten David and Verdonck Tim}
}
