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TitleComparing Text Mining Algorithms for Predicting the Severity of a Reported Bug
Author(s)Ahmed Lamkanfi, Serge Demeyer, Quinten David Soetens and Tim Verdonck
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AbstractA 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
BibTeX
@inproceedings{LamkanfiCSMR2011,
	author = {Ahmed Lamkanfi, Serge Demeyer, Quinten David Soetens and Tim Verdonck},
	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},
	note = {}
}
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 Lab On REengineering - Antwerpen, last modified 11:04:52 16 March 2011