INTELLIGENT VULNERABILITY PRIORITIZATION USING MACHINE LEARNING AND SOFTWARE TESTING ANALYTICS

Abstract

The increasing complexity of modern software systems and the continuous emergence of cyber threats have created significant challenges in identifying and prioritizing security vulnerabilities effectively. Traditional vulnerability management approaches often generate large numbers of security findings, making it difficult for organizations to determine which vulnerabilities require immediate attention. This paper proposes an Intelligent Vulnerability Prioritization Framework Using Machine Learning and Software Testing Analytics by integrating machine learning, software testing metrics, vulnerability intelligence, risk assessment, and automated security analytics. The proposed framework analyzes vulnerability characteristics, source code behavior, software testing outcomes, exploit information, and system impact factors to intelligently rank security risks. Machine learning models classify vulnerabilities according to severity, exploitability, and business impact, enabling efficient remediation planning. Software testing analytics provide additional insights from code coverage, defect history, and testing performance. Experimental evaluation demonstrates improvements in vulnerability ranking accuracy, remediation efficiency, risk visibility, and security resource optimization. The proposed framework supports proactive and intelligent vulnerability management in modern software environments. Keywords— Vulnerability Prioritization, Machine Learning, Software Testing Analytics, Risk Assessment, Cybersecurity, DevSecOps, Security Analytics, Intelligent Automation.