Bug Prioritization to Facilitate Bug Report Triage |
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| Authors: | Jaweria Kanwal Onaiza Maqbool |
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| Affiliation: | (1) Department of Computer Science, Quaid-i-Azam University, Islamabad, Pakistan |
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| Abstract: | The large number of new bug reports received in bug repositories of software systems makes their management a challenging
task. Handling these reports manually is time consuming, and often results in delaying the resolution of important bugs. To
address this issue, a recommender may be developed which automatically prioritizes the new bug reports. In this paper, we
propose and evaluate a classification based approach to build such a recommender. We use the Na?ve Bayes and Support Vector
Machine (SVM) classifiers, and present a comparison to evaluate which classifier performs better in terms of accuracy. Since
a bug report contains both categorical and text features, another evaluation we perform is to determine the combination of
features that better determines the priority of a bug. To evaluate the bug priority recommender, we use precision and recall
measures and also propose two new measures, Nearest False Negatives (NFN) and Nearest False Positives (NFP), which provide
insight into the results produced by precision and recall. Our findings are that the results of SVM are better than the Na?ve
Bayes algorithm for text features, whereas for categorical features, Na?ve Bayes performance is better than SVM. The highest
accuracy is achieved with SVM when categorical and text features are combined for training. |
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| Keywords: | bug triaging bug priority classification mining bug repositories evaluation measures |
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