Towards an Improvement of Bug Severity Classification

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 wi...

Full description

Saved in:
Bibliographic Details
Main Authors: Kanti-Singha Roy, Nivir, Rossi, Bruno
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Bug Severity Classification
Computer bugs
Feature extraction
Feature Selection
Niobium
Testing
Text mining
Training
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 Naïve 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.
ISSN:1089-6503
2376-9505
DOI:10.1109/SEAA.2014.51