Towards an Improvement of Bug Severity Classification

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Publikace nespadá pod Ekonomicko-správní fakultu, ale pod Fakultu informatiky. Oficiální stránka publikace je na webu muni.cz.
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SINGHA ROY Nivir Kanti ROSSI Bruno

Rok publikování 2014
Druh Článek ve sborníku
Konference 40th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2014
Fakulta / Pracoviště MU

Fakulta informatiky

Citace
Doi http://dx.doi.org/10.1109/SEAA.2014.51
Obor Informatika
Klíčová slova Bug Severity Classification; Text Mining; Feature Selection;
Popis 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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