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Automatic Bug Classification System to Improve the Software Organization Product Performance

Author

Listed:
  • A. R. Darshika Kelin

    (St Joseph's Institute of Technology, India)

  • B. Nagarajan

    (Arunai Engineering College, Tiruvannamalai, India)

  • Sasikumar Rajendran

    (K. Ramakrishnan College of Engineering, India)

  • Muthumari S.

    (S.S. Duraisamy Nadar Mariammal College, Kovilpatti, India)

Abstract

Consistently, many bugs are raised, which are not completely settled, and countless designers are utilizing open sources or outsider assets, which prompts security issues. Bug-triage is the impending mechanized bug report framework to appoint individual security teams for a more than adequate pace of bug reports submitted from various IDEs inside the association (on-premises). We can lessen the time and cost of bug following and allocate it to the fitting group by foreseeing which division it has a place in within an association. In this paper, the authors are executing an automatic bug tracking system (ABTS) to allocate the group for the revealed bug involving the text examination for bug naming and characterization AI calculation for anticipating designer. Hybrid natural language processing and machine learning techniques are used for automatic bug identification to improve the performance of software organization products.

Suggested Citation

  • A. R. Darshika Kelin & B. Nagarajan & Sasikumar Rajendran & Muthumari S., 2022. "Automatic Bug Classification System to Improve the Software Organization Product Performance," International Journal of Sociotechnology and Knowledge Development (IJSKD), IGI Global, vol. 14(1), pages 1-17, January.
    • Handle: RePEc:igg:jskd00:v:14:y:2022:i:1:p:1-17
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