Static Code Vulnerability Detection Framework for Secure Compiler Assisted Software Development Pipelines

With the growing use of software systems in critical areas, securing the code while it is being developed has become a top priority. Traditional manual code reviews and testing can't often find subtler vulnerabilities for a significant security potential risk. This study fills the gaps of automated and accurate detection of static code vulnerabilities in secure compiler-aided software development pipelines. The goal is to have a strong structure that combines the static code analysis, the anomaly detection using machine learning, and the compiler-based instrumentation in order to detect potential vulnerabilities early in the development. The proposed framework uses the combinations of hybrid techniques of pattern-based vulnerability scanning, control-flow and data-flow analysis, and reinforcement learning models for prioritizing critical issues. Experiments were performed on commonly used benchmark datasets, such as the Juliet Test Suite, among several programming languages, and the detection accuracy of 96.3%, precision of 94.8%, recall of 95.1%, and F1-score of 94.9% were obtained, which were 8−12% better than existing static analysis tools on all metrics. The results show the effectiveness of making compiler-level insights work along with machine learning models in proactive vulnerability detection. Conclusively it does not only improve the security posture of software development pipeline but also lowers the overhead of remediation of vulnerability post deployment which is a practical solution for secure software engineering