Towards an Improvement of Bug Severity Classification

被引：36

作者：

Roy, Nivir Kanti Singha ^{[1
]}

Rossi, Bruno ^{[2
]}

机构：

[1] Free Univ Bozen Bolzano, I-39100 Bozen Bolzano, Italy

[2] Masaryk Univ, Dept Comp Syst & Commun, Brno, Czech Republic

来源：

2014 40TH EUROMICRO CONFERENCE SERIES ON SOFTWARE ENGINEERING AND ADVANCED APPLICATIONS (SEAA 2014) | 2014年

关键词：

Bug Severity Classification; Text Mining; Feature Selection; SOFTWARE;

D O I：

10.1109/SEAA.2014.51

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

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.

引用

页码：269 / 276

页数：8

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