Towards an Improvement of Bug Severity Classification
| Authors | |
|---|---|
| Year of publication | 2014 |
| Type | Article in Proceedings |
| Conference | 40th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2014 |
| MU Faculty or unit | |
| 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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