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

Author

Listed:
  • Anh-Hien Dao

    (Department of Computer Science and Engineering, Yuan Ze University, 135 Yuan-Tung Road, Taoyuan 320315, Taiwan)

  • Cheng-Zen Yang

    (Department of Computer Science and Engineering, Yuan Ze University, 135 Yuan-Tung Road, Taoyuan 320315, Taiwan)

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.

Suggested Citation

  • Anh-Hien Dao & Cheng-Zen Yang, 2021. "Severity Prediction for Bug Reports Using Multi-Aspect Features: A Deep Learning Approach," Mathematics, MDPI, vol. 9(14), pages 1-16, July.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:14:p:1644-:d:593244
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    References listed on IDEAS

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    1. Meera Sharma & Madhu Kumari & V. B. Singh, 2019. "Multi-attribute dependent bug severity and fix time prediction modeling," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(5), pages 1328-1352, October.
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