A Review of Automated Software Bug Severity Prediction from Traditional to Advanced Models

Authors

  • Sweety Ahlawat, Dhiraj Khurana

Keywords:

Bug Severity, Machine Learning, Deep Learning, Bug Features, Severity Prediction

Abstract

Predicting the severity of bugs is an important part of software quality assurance throughout both the development and maintenance phases, so developers may better utilize their resources. An exhaustive evaluation of software defect severity assessment models using machine learning, deep learning, and statistical techniques is presented in this work. In the context of software maintenance, severity refers to the efficacy, resistance, and hindrance of bugs. Additionally, this study delves into the methods used for prediction and various bug attributes. It discusses popular data repositories, pipeline workflows, severity techniques, and current research directions based on the existing system. Evaluate several feature sets based on their performance in predicting problem severity levels, including model complexity, generalizability, and data availability.

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Published

26.03.2024

How to Cite

Sweety Ahlawat. (2024). A Review of Automated Software Bug Severity Prediction from Traditional to Advanced Models. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 4575 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/8436

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

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