Abstract
The performance of ten classic algorithms to classify the software bugs for different bug repositories are compared. The algorithms included in the study are Naïve Bayes, Naïve Bayes Multinomial, Discriminative Multinomial Naïve Bayes (DMNB), J48, Support Vector Machine, Radial Basis Function (RBF) Neural Network, Classification using Clustering, Classification using Regression, Adaptive Boosting (AdaBoost) and Bagging. These algorithms are applied on four open source bug repositories namely Android, JBoss-Seam, Mozilla and MySql. The classification is evaluated using 10-fold cross validation technique. The accuracy and F-measure parameters are compared for all of the algorithms. The concept of software bug taxonomy hierarchy is also introduced with eleven standard bug categories (classes). The comparative study also covers the effect of number of categories over performance of classifiers in terms of accuracy and F-measure. The results are produced in tabular and graphical forms.
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