RISK-AWARE SOFTWARE TESTING FRAMEWORK FOR EARLY IDENTIFICATION OF SECURITY VULNERABILITIES

Abstract

The increasing complexity of modern software systems and the rapid adoption of continuous development practices have intensified the challenge of identifying security vulnerabilities at early stages of the software lifecycle. Traditional software testing approaches primarily focus on functional correctness and often provide limited capabilities for assessing security risks during development. This paper proposes a Risk-Aware Software Testing Framework for Early Identification of Security Vulnerabilities by integrating machine learning, security analytics, software testing metrics, vulnerability intelligence, and risk assessment techniques. The proposed framework analyzes source code characteristics, software dependencies, testing outcomes, historical defect information, and security indicators to identify potential vulnerabilities before deployment. A risk-aware prioritization mechanism evaluates vulnerabilities based on severity, exploitability, application importance, and business impact. The framework enables continuous security testing within DevSecOps pipelines and supports proactive vulnerability management. Experimental evaluation demonstrates improvements in early vulnerability detection, risk classification accuracy, testing efficiency, and secure software development practices. The proposed approach provides an intelligent solution for reducing security risks during software engineering processes. Keywords— Risk-Aware Testing, Software Vulnerability Detection, Machine Learning, Security Testing, DevSecOps, Vulnerability Assessment, Secure Software Development, Risk Analytics.