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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 a 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, s.~269-276. ISBN~978-1-4799-5794-1. Dostupné z: https://dx.doi.org/10.1109/SEAA.2014.51.
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