Enhancement Of Mutation-Based Fuzzing Methods

Fuzz testing is a form of automated testing that employs random or unusual inputs to search for unusual system behaviors. Fuzzing is a fundamental method that finds software weaknesses, but previously it struggled with creating organized inputs despite substantial progress. So, we decided to examine the current methodologies of mutation-based fuzz testing and their vulnerabilities. In this article, a well-known fuzzing tool was tested to evaluate the proposed model and compare its performance with the existing models and the results showed that it performs better. The proposed model enhances code coverage by employing advanced techniques to systematically explore diverse execution paths and detect deeper vulnerabilities. Additionally, our model incorporates a novel approach to processing test cases, which optimizes mutation strategies and improves overall testing efficiency. This contribution not only improves mutation-based fuzzing but also tackles its shortcomings, thereby propelling the conversation on software security by enhancing methods for identifying vulnerabilities.