Towards an Improvement of Bug Severity Classification

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Authors

SINGHA ROY Nivir Kanti ROSSI Bruno

Year of publication 2014
Type Article in Proceedings
Conference 40th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2014
MU Faculty or unit

Faculty of Informatics

Citation
Doi http://dx.doi.org/10.1109/SEAA.2014.51
Field Informatics
Keywords Bug Severity Classification; Text Mining; Feature Selection;
Description 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.
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