Publication
Title
Comparing mining algorithms for predicting the severity of a reported bug
Author
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.
Language
English
Source (book)
15th European Conference on Software Maintenance and Reengineering, (CSMR), March 1-4, 2011, Oldenburg, Germany
Publication
Los Alamitos, Calif. : IEEE, 2011
Volume/pages
p. 249-258
ISI
000299256500029
Number
978-0-7695-4343-7
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Research group
[E?say:metaLocaldata.cgzprojectinf]
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 18.04.2012
Last edited 07.08.2017
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