Bug reports identification using multiclassification method
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Abstract
Whenever software defects (or bugs) are detected, they must be fixed immediately to allow the software to perform properly. The classification task for bug reports includes not only binary classification but also multiclassification. Therefore, multiclassification for bug reports was chosen as the challenge in this study. The proposed method aimed to classify bug reports into three classes, namely real-bug, enhancement, and task. The method began with bug report pre-processing, and then the vector of bug reports was used to develop the multiclassifier models. Eight machine learning algorithms namely multinomial naïve Bayes, logistic regression, random forest, support vector machines, k-nearest neighbor, extreme gradient boosting, neural networks and decision trees were compared. Finally, the classifier was chosen as the best model for the proposed method, and compared with the baseline. The Matthews correlation coefficient, area under the curve, F1 and accuracy scores of the best classifier from the proposed method showed improvement from the baseline at 4.09%, 2.71%, 1.83% and 1.69%, respectively.
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