Severity Prediction for Bug Reports Using Multi-Aspect Features: A Deep Learning Approach

Abstract

The severity of software bug reports plays an important role in maintaining software quality. Many approaches have been proposed to predict the severity of bug reports using textual information. In this research, we propose a deep learning framework called MASP that uses convolutional neural networks (CNN) and the content-aspect, sentiment-aspect, quality-aspect, and reporter-aspect features of bug reports to improve prediction performance. We have performed experiments on datasets collected from Eclipse and Mozilla. The results show that the MASP model outperforms the state-of-the-art CNN model in terms of average Accuracy, Precision, Recall, F1-measure, and the Matthews Correlation Coefficient (MCC) by 1.83%, 0.46%, 3.23%, 1.72%, and 6.61%, respectively.

Keywords

• bug reports
• severity prediction
• multi-aspect features
• deep learning