Fuzzing Testing Automation Using Genetic Algorithm

Software testing plays a vital role in ensuring the reliability, security, and quality of modern software systems. As software systems grow more complex, traditional testing techniques often fail to detect hidden vulnerabilities and unexpected behaviors. One of the most effective automated testing techniques used today is fuzz testing, also known as fuzzing. Fuzz testing involves providing large amounts of random, unexpected, or malformed input data to a program in order to discover software bugs, crashes, security vulnerabilities, and unexpected behavior. This project focuses on improving the efficiency and effectiveness of fuzz testing by integrating it with Genetic Algorithms (GA), a powerful optimization technique inspired by natural evolution.The main objective of this project is to develop an intelligent fuzz testing system that uses a genetic algorithm to automatically generate and evolve test inputs for software programs. Traditional fuzz testing methods rely mostly on purely random input generation, which may require a large amount of time to discover critical vulnerabilities. In contrast, the proposed approach uses a genetic algorithm to guide the input generation process toward more promising test cases. By simulating the principles of natural selection such as selection, crossover, and mutation, the genetic algorithm evolves input data over multiple generations, gradually improving the ability to detect software faults.