Detailed Information on Publication Record
2014
Towards an Improvement of Bug Severity Classification
SINGHA ROY, Nivir Kanti and Bruno ROSSIBasic information
Original name
Towards an Improvement of Bug Severity Classification
Authors
SINGHA ROY, Nivir Kanti (380 Italy) and Bruno ROSSI (380 Italy, guarantor, belonging to the institution)
Edition
Verona, 40th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2014, p. 269-276, 8 pp. 2014
Publisher
IEEE
Other information
Language
English
Type of outcome
Stať ve sborníku
Field of Study
10201 Computer sciences, information science, bioinformatics
Country of publisher
United States of America
Confidentiality degree
není předmětem státního či obchodního tajemství
Publication form
printed version "print"
RIV identification code
RIV/00216224:14330/14:00076796
Organization unit
Faculty of Informatics
ISBN
978-1-4799-5794-1
UT WoS
000358153200041
Keywords in English
Bug Severity Classification; Text Mining; Feature Selection;
Tags
Tags
International impact, Reviewed
Změněno: 28/4/2015 11:31, RNDr. Pavel Šmerk, Ph.D.
Abstract
V originále
Predicting the severity of bugs has been found in past research to improve triaging and the bug resolution process. For this reason, many classification/prediction approaches emerged over the years to provide an automated reasoning over severity classes. In this paper, we use text mining together with bi-grams and feature selection to improve the classification of bugs in severe/non-severe classes. We adopt the Naive Bayes (NB) classifier considering Mozilla and Eclipse datasets commonly used in related works. Overall, the results show that the application of bi-grams can improve slightly the performance of the classifier, but feature selection can be more effective to determine the most informative terms and bi-grams. The results are in any case project-dependent, as in some cases the addition of bi-grams may worsen the performance.
Links
| LG13010, research and development project |
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