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@inproceedings{1202165,
author = {Singha Roy, Nivir Kanti and Rossi, Bruno},
address = {Verona},
booktitle = {40th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2014},
doi = {http://dx.doi.org/10.1109/SEAA.2014.51},
keywords = {Bug Severity Classification; Text Mining; Feature Selection;},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Verona},
isbn = {978-1-4799-5794-1},
pages = {269-276},
publisher = {IEEE},
title = {Towards an Improvement of Bug Severity Classification},
year = {2014}
}
TY - JOUR
ID - 1202165
AU - Singha Roy, Nivir Kanti - Rossi, Bruno
PY - 2014
TI - Towards an Improvement of Bug Severity Classification
PB - IEEE
CY - Verona
SN - 9781479957941
KW - Bug Severity Classification
KW - Text Mining
KW - Feature Selection;
N2 - 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.
ER -
SINGHA ROY, Nivir Kanti and Bruno ROSSI. Towards an Improvement of Bug Severity Classification. In \textit{40th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2014}. Verona: IEEE, 2014, p.~269-276. ISBN~978-1-4799-5794-1. Available from: https://dx.doi.org/10.1109/SEAA.2014.51.
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