Applying Machine Learning Techniques for Early Detection and Prevention of Software Vulnerabilities

Abstract

In the rapidly advancing software development field, maintaining high standards of quality assurance (QA) and managing risks effectively is crucial. Traditional methods are often reactive, addressing issues only after defects emerge, resulting in costly delays. This paper explores how machine learning (ML) algorithms can transform QA practices by enabling predictive defect tracking and dynamic risk management. By analyzing historical data, ML can predict defect-prone areas, assess risks in real-time, and optimize testing strategies, thereby enhancing software reliability. This paper also compares various ML algorithms, including decision trees, support vector machines, and neural networks, highlighting their effectiveness in defect prediction and risk management.