Multiclass Software Bug Severity Classification using Decision Tree, Naive Bayes and Bagging
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Abstract
The software applications are experiencing the challenges of ever-growing complexity caused by the increase in the number of bugs. The software development process has been adversely affected due to the wastage of resources caused due to the bugs. It is imperative to identify and predict bugs to facilitate the software development process. Software bugs can be classified according to the severity of the bugs. In this paper a comparative analysis of Decision Tree, Naïve Bayes and Bagging approach is done for the bug severity classification. A comparative analysis of the Naïve Bayes, Decision Tree and Bagging approach is done for the accuracy, precision, recall and F-measure parameters
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