Software Vulnerability Fuzz Testing: A Mutation-Selection Optimization Systematic Review

Abstract

As software vulnerabilities can cause cybersecurity threats and have severe consequences, it is necessary to develop effective techniques to discover such vulnerabilities. Fuzzing is one of the most widely employed approaches that has been adapted for software testing. The mutation-based fuzzing approach is currently the most popular. The state-of-the-art American Fuzzy Lop (AFL) selects mutations randomly and lacks knowledge of mutation operations that are more helpful in a particular stage. This study performs a systematic review to identify and analyze existing approaches that optimize the selection of mutation operations. The main contributions of this work are to draw attention to the importance of mutation operator selection, identify optimization algorithms for mutation operator selection, and investigate their impact on fuzzing testing in terms of code coverage and finding new vulnerabilities. The investigation shows the effectiveness and advantages of optimizing the selection of mutation operations to achieve higher code coverage and find more vulnerabilities.