Using CNN to Predict the Resolution Status of Bug Reports

Abstract

Bug tracking systems (BTS) are a resource for receiving bug reports that help to improve software applications. They usually contain reports reported by the end-users or developers. Bug Reports contain some suggestions, complaints, etc. The problem is that every submitted bug report is not accepted to implement. Mostly bug reports are rejected because they are incomplete, duplicate, expired, etc. while only a few are accepted to implement. But the developers have to check every bug report manually that needs many resources (i.e. labour, money, time). In this study, we proposed a convolutional neural network (CNN) based approach to automatically classify bug reports as accepted and rejected. Results show that the proposed approach achieves the highest performance as compared to closely related works.