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International Journal of Information Security Engineering Review Article

Strategy for Improving Software Maintenance Using Machine Learning for Security Requirements: A Review

  1. Maitri Manya Department of Computer Science and Engineering, Lakshmi Narain College of Technology, Bhopal
  2. Raj Kumar Sharma Department of Computer Science and Engineering, Lakshmi Narain College of Technology, Bhopal

Abstract

Within the area of software technical education, the significance of software defect discovery has increased as a research focus to enhance program reliability. By maximizing testing resources and assisting developers in identifying potential problems using program defect predictions, program dependability is increased. Applying software engineering (SE) techniques to critical and intricate systems, like networking and security systems, is imperative. Traditional methods of predicting software maintainability have limitations, particularly in balancing security concerns, maintainability, and system integrity. This work explores the application of machine learning (ML) techniques to predict and improve software maintainability by identifying key software metrics. The study explores several ML models, including deep learning, to increase the precision of the predictions made by software maintainability metrics. It also reviews existing research on software maintainability and defect prediction, identifying common research gaps such as model scalability, interpretability, and class imbalance issues. By employing ML classification techniques and addressing these gaps, this study aims to bridge the gap between security considerations and maintainability, providing more robust and efficient methods for software maintenance.

Keywords

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