Machine Learning and Just-in-Time Strategies for Effective Bug Tracking in Software Development

Authors

  • 1Veena Jadhav Assistant Professor, Department of Computer Engineering, Bharati Vidyapeeth(Deemed to be University) College of Engineering, Pune, Maharashtra, India
  • Prakash Devale Department of Information Technology, Bharati Vidyapeeth(Deemed to be university) College of Engineering, Pune, Maharashtra, India
  • Rohini B. Jadhav Associate Professor, Department of Information Technology, Bharati Vidyapeeth(Deemed to be university) College of Engineering, Pune, Maharashtra, India
  • Ranjeet Vasant Bidwe Symbiosis Institute of Technology, Pune (SIT), Symbiosis International (Deemed)University (SIU), Lavale, Pune, Maharashtra, India
  • Madhavi Mane Assistant Professor, Department of Computer Engineering, Bharati Vidyapeeth(Deemed to be University) College of Engineering, Pune, Maharashtra, India
  • Prajakta Pawar Assistant Professor, Bharati Vidyapeeth's College of Engineering Lavale Pune, Maharashtra, India

Keywords:

Bug tracking, Just in Time (JIT), Machine Learning, Software fault prediction, Software development

Abstract

Effective bug tracking and resolution are crucial for maintaining software quality and ensuring timely project delivery in the constantly changing field of software development.  This research paper introduces an innovative method that combines machine learning techniques with just-in-time (JIT) strategies to improve bug tracking and resolution processes. In the study JM1 dataset is used for software defect prediction. This work also introduces a comprehensive feature engineering methodology to extract relevant information from the dataset.  The study proposed a hybrid model that incorporate Random Forest and Support Vector Machine (SVM) classifiers, to forecast and rank software defects according to different bug attributes. The proposed model exhibits a remarkable accuracy of 98.79%, thereby demonstrating its efficacy in precisely detecting and prioritizing bugs.   The exceptional level of precision is credited to the robust feature engineering method, which considers complexity metrics and historical defect density. The research highlights the importance of promptly addressing newly reported bugs by implementing just-in-time (JIT) principles in bug tracking practices. This involves assigning and prioritizing bugs in real-time within the current development cycle.   The integration of JIT and machine learning optimizes the software development process by reducing delays, speeding up problem-solving, and improving overall efficiency. The research findings offer valuable insights for software development teams aiming to enhance the efficiency of their bug tracking procedures.  The combination of the Random Forest and SVM model, enhanced by JIT strategies, offers a highly effective framework for guaranteeing software quality and timely project completion in the rapidly evolving field of software development.  This research provides a current and pragmatic method for staying ahead of software defects, as the software industry continues to progress.

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Published

30.11.2023

How to Cite

1Veena Jadhav, Devale, P. ., Jadhav, R. B. ., Bidwe, R. V. ., Mane, M. ., & Pawar, P. . (2023). Machine Learning and Just-in-Time Strategies for Effective Bug Tracking in Software Development. International Journal of Intelligent Systems and Applications in Engineering, 12(6s), 749–758. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/4013

Issue

Vol. 12 No. 6s (2024)

Section

Research Article

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