Automatic Bug Priority Prediction Using DNN Based Regression

  • Wei ZhangEmail author
  • Chris Challis
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1074)


Bugs are inevitable during software development. It is important to prioritize bugs and fix them based on their priorities. The priority assignment is usually done manually. Besides the cost of human effort, this process may also introduce bias since different people might have different opinions on the same issue. In this paper, we propose an approach to automate the process. It builds features from bug reports using Natural Language Processing, then trains a predictive model based on a deep Neural Network. The proposed approach was tested using a comprehensive data set containing more than 82 thousand bug reports. It runs in near real-time and its performance is significantly better than the previously reported results.


  1. 1.
    Anvik, J., Murphy, G.C.: Reducing the effort of bug report triage: recommenders for development-oriented decisions. ACM Trans. Softw. Eng. Methodol. (TOSEM) 20(3), 10 (2011)CrossRefGoogle Scholar
  2. 2.
    Arora, R., Basu, A., Mianjy, P., Mukherjee, A.: Understanding deep neural networks with rectified linear units. arXiv preprint arXiv:1611.01491 (2016)
  3. 3.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(8), 993–1022 (2003)zbMATHGoogle Scholar
  4. 4.
    Breiman, L.: Stacked regressions. Mach. Learn. 24(1), 49–64 (1996)zbMATHGoogle Scholar
  5. 5.
    Choudhary, P.A.: Neural network based bug priority prediction model using text classification techniques. Int. J. Adv. Res. Comput. Sci. 8(5) (2017)Google Scholar
  6. 6.
    He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263–1284 (2009)CrossRefGoogle Scholar
  7. 7.
    Lamkanfi, A., Demeyer, S., Giger, E., Goethals, B.: Predicting the severity of a reported bug. In: 2010 7th IEEE Working Conference on Mining Software Repositories (MSR), pp. 1–10. IEEE (2010)Google Scholar
  8. 8.
    Lamkanfi, A., Demeyer, S., Soetens, Q.D., Verdonck, T.: Comparing mining algorithms for predicting the severity of a reported bug. In: 15th European Conference on Software Maintenance and Reengineering (CSMR), pp. 249–258. IEEE (2011)Google Scholar
  9. 9.
    Linares-Vásquez, M., Hossen, K., Dang, H., Kagdi, H., Gethers, M., Poshyvanyk, D.: Triaging incoming change requests: bug or commit history, or code authorship? In: 28th IEEE International Conference on Software Maintenance (ICSM) (2012)Google Scholar
  10. 10.
    Menzies, T., Marcus, A.: Automated severity assessment of software defect reports. In: IEEE International Conference on Software Maintenance, ICSM 2008, pp. 346–355. IEEE (2008)Google Scholar
  11. 11.
    Murphy, G., Cubranic, D.: Automatic bug triage using text categorization. In: Proceedings of the Sixteenth International Conference on Software Engineering and Knowledge Engineering (2004)Google Scholar
  12. 12.
    Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pp. 807–814 (2010)Google Scholar
  13. 13.
    Shokripour, R., Anvik, J., Kasirun, Z.M., Zamani, S.: Why so complicated? Simple term filtering and weighting for location-based bug report assignment recommendation. In: Mining Software Repositories (MSR), pp. 2–11 (2013)Google Scholar
  14. 14.
    Shrivastava, A., Gupta, A., Girshick, R.: Training region-based object detectors with online hard example mining. In: Computer Vision and Pattern Recognition (CVPR), pp. 761–769 (2016)Google Scholar
  15. 15.
    Tian, Y., Lo, D., Sun, C.: Information retrieval based nearest neighbor classification for fine-grained bug severity prediction. In: 2012 19th Working Conference on Reverse Engineering (WCRE), pp. 215–224. IEEE (2012)Google Scholar
  16. 16.
    Tian, Y., Lo, D., Xia, X., Sun, C.: Automated prediction of bug report priority using multi-factor analysis. Empirical Softw. Eng. 20, 1354–1383 (2015)CrossRefGoogle Scholar
  17. 17.
    Xia, X., Lo, D., Wang, X., Zhou, B.: Dual analysis for recommending developers to resolve bugs. J. Softw.: Evol. Process 27(3), 195–220 (2015)Google Scholar
  18. 18.
    Ye, X., Bunescu, R., Liu, C.: Learning to rank relevant files for bug reports using domain knowledge. In: Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering, pp. 689–699. ACM (2014)Google Scholar
  19. 19.
    Zhou, J., Zhang, H., Lo, D.: Where should the bugs be fixed? More accurate information retrieval-based bug localization based on bug reports. In: 2012 34th International Conference on Software Engineering (ICSE), pp. 14–24. IEEE (2012)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Adobe Systems Inc.McLeanUSA

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