Serge Demeyer | Publications | E-mail Feedback
Last updated on Thursday, November 16, 2023
@inproceedings{Lamkanfi2011CSMR,
author = {Lamkanfi, Ahmed and Demeyer, Serge and
Soetens, Quinten David and Verdonck Tim},
booktitle = {Proceedings {CSMR}'2011 (15th European Conference on
Software Maintenance and Reengineering)},
month = mar,
note = {Acceptance ratio: 29/101 = 28.7.4\%},
publisher = {{IEEE} Press},
title = {Comparing Text Mining Algorithms for Predicting the
Severity of a Reported Bug},
year = {2011},
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.},
annote = {internationalconference},
}