False Positive Elimination in SAST Tools: Techniques for Improved Accuracy
Keywords:
Static Application Security Testing, SAST tools, false positives, machine learning, hybrid techniques, software security, vulnerability detection, Author name, scopus, springer, Journal Name, Wissira, Journal Short Form, Wissira Press, Wissira Research Lab, Research Press, SSRN, ISSN, Academia, UGC Care, PubMed, WOSAbstract
Static Application Security Testing (SAST) tools play a critical role in software security by identifying vulnerabilities in source code early in the development process. However, their utility is often hampered by the high rate of false positives—incorrect identification of secure code as vulnerable. False positives increase developer workload and erode trust in SAST tools. This manuscript explores the sources of false positives and presents state-of-the-art techniques for their elimination, including machine learning (ML), advanced static analysis techniques, and hybrid methodologies. Experimental results show that implementing these techniques can significantly reduce false positives without sacrificing detection accuracy, enabling more efficient and reliable software development workflows.
