EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR AUTOMATED SOFTWARE VULNERABILITY PREDICTION AND RISK CLASSIFICATION

Abstract

Software vulnerabilities have become a major security concern due to increasing software complexity, rapid development cycles, and the continuous evolution of cyber threats. Traditional vulnerability assessment approaches often rely on manual analysis and rule-based security tools, which may fail to identify complex vulnerabilities and provide limited insights into risk factors. This paper proposes an Explainable Artificial Intelligence Framework for Automated Software Vulnerability Prediction and Risk Classification by integrating machine learning, deep learning, explainable AI (XAI), static code analysis, software metrics, and intelligent security analytics. The proposed framework analyzes source code characteristics, software dependencies, historical vulnerability information, and security patterns to predict potential vulnerabilities and classify their associated risks. Explainability mechanisms provide transparent insights into model predictions, enabling developers and security analysts to understand vulnerability causes and prioritize remediation activities. Experimental evaluation demonstrates improvements in vulnerability prediction accuracy, risk classification performance, interpretability, and automated security assessment. The proposed framework provides a trustworthy solution for intelligent software security management.