CNN - Based Priority Prediction of Bug Reports

When considering software maintenance, priority prediction is an essential part of it. Thousands of bugs are reported daily in the Bug Tracking System (BTS); Bugzilla, JIRA, and GitHub are commonly used. Priority assignment for the reported bugs is conducted manually. Therefore, this task takes considerable time to do, and there is also a high possibility of making a mistake. Therefore, it is imperative to have a way to predict the bug report's priority automatically. Our study proposed a model based on the Convolutional Neural Network (CNN) to predict the bug report's priority. First, preprocess the textual content in bug reports using natural language processing (NLP) approaches. Then extract the features from the textual context (short description) using the Bag-of-word feature extraction method. Finally, train a CNN-based classifier to make priority predictions based on its input. Then our result is compared with the Support Vector Machine (SVM) and Temporal Convolutional Network (TCN) to find a better model for priority prediction. The final results show that the proposed approach based on the CNN classifier performs better than the other approaches, and it shows a 71% accuracy while others have low accuracy, like 63% and 48% for SVM and TCN, respectively. The proposed model's performance was evaluated using the Bugzilla dataset, which included over 25,000 bug reports.