Review on Automated Test Data Generation Using Computational Intelligence Techniques

Abstract Software testing is the necessity in the field of software development and its needs to work more effectively in order to develop error free software. The major difficulty was obtained in the development process is generating test data, is provided input to the component under test. Previously many researchers have been done in order to reduce time and bug in the manual testing phase. Henceforth researchers were focusing on the evolutionary techniques. Somehow many of the evolutionary technique work properly and the result was up to the makeable label. But the bugs were obtained in that process was not resolved properly. After analysis, the hybrid approach was working somehow good in order to generate test data. We use some population-based meta-heuristics algorithm as an illustration Particle Swarm Optimization, Differential Evolution, and Genetic Algorithms to solve this problem. These techniques are followed by path coverage and maximum fitness.