REINFORCING SOFTWARE VERIFICATION: STATIC CODE ANALYSIS FRAMEWORKS AND TOOLCHAINS

Abstract

      Static code analysis tools are pivotal for identifying and mitigating software vulnerabilities, which significantly reduces development costs and enhances efficiency. By automating the detection of potential issues, these tools eliminate the need for extensive manual code reviews and streamline the development process, allowing programmers to focus on creating robust and secure software solutions. This paper explores the capabilities of static code analysis tools, particularly their role in detecting common software vulnerabilities. A comparative study evaluates various tools based on scalability, accuracy, usability within integrated development environments (IDEs) and optimizing software verification processes. The findings reveal that combining multiple static analysis techniques, such as abstract interpretation, data flow analysis, and program slicing can enhance software reliability and security. Machine learning approaches, including clustering for categorizing similar bugs and supervised learning for identifying vulnerabilities can be the part of tool to enhance the accuracy and effectiveness of these tools.