Title
Comparing mining algorithms for predicting the severity of a reported bug Comparing mining algorithms for predicting the severity of a reported bug
Author
Faculty/Department
Faculty of Sciences. Mathematics and Computer Science
Publication type
conferenceObject
Publication
Los Alamitos, Calif. :IEEE, [*]
Subject
Computer. Automation
Source (book)
15th European Conference on Software Maintenance and Reengineering, (CSMR), March 1-4, 2011, Oldenburg, Germany
ISBN
978-0-7695-4343-7
ISI
000299256500029
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
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.
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