Abstract
Developers across most of the organizations face the issue of manually dealing with the classification of the software bug reports. Software bug reports often contain text and other useful information that are common for a particular type of bug. This information can be extracted using the techniques of Natural Language Processing and combined with the manual classification done by the developers until now to create a properly labelled data set for training a supervised learning model for automatically classifying the bug reports into their respective categories. Previous studies have only focused on binary classification of software incident reports as bug and non-bug. Our novel approach achieves an accuracy of 76.94% for a 10-factor classification problem on the bug repository created by Microsoft Dynamics 365 team. In addition, we propose a novel method for automatically retraining the model and updating it with developer feedback in case of misclassification that will significantly reduce the maintenance cost and effort.
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