A Multi-label, Dual-Output Deep Neural Network for Automated Bug Triaging

@article{ChoquetteChoo2019AMD,
  title={A Multi-label, Dual-Output Deep Neural Network for Automated Bug Triaging},
  author={Christopher A. Choquette-Choo and David Sheldon and Jonny Proppe and John Alphonso-Gibbs and Harsha Gupta},
  journal={2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)},
  year={2019},
  pages={937-944}
}
  • Christopher A. Choquette-Choo, David Sheldon, +2 authors Harsha Gupta
  • Published 2019
  • Computer Science
  • 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)
  • Bug tracking enables the monitoring and resolution of issues and bugs within organizations. Bug triaging, or assigning bugs to the owner(s) who will resolve them, is a critical component of this process because there are many incorrect assignments that waste developer time and reduce bug resolution throughput. In this work, we explore the use of a novel two-output deep neural network architecture (Dual DNN) for triaging a bug to both an individual team and developer, simultaneously. Dual DNN… CONTINUE READING

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 20 REFERENCES

    Automated bug assignment: Ensemble-based machine learning in large scale industrial contexts

    VIEW 4 EXCERPTS
    HIGHLY INFLUENTIAL

    Improving bug triage with bug tossing graphs

    VIEW 5 EXCERPTS
    HIGHLY INFLUENTIAL

    Automatic Bug Assignment Using Information Extraction Methods

    VIEW 2 EXCERPTS

    Who should fix this bug?

    VIEW 3 EXCERPTS