Performance Evaluation of GSA and PSO Based Algorithms for Automated Test Data Generation for Software

In this Dissertation, we have implemented Meta-heuristic based search algorithms namely PSO and GSA algorithms for automatic test case generation using path testing criterion. For generation of test cases, symbolic execution method has been used in which first, target path is selected from Control Flow Graph (CFG) of Software under Test (SUT) and then inputs are generated using search algorithms which can evaluate composite predicate corresponding to the target path true. We have experimented on two real world programs showing the applicability of these techniques in genuine testing environment. The algorithm is implemented using MATLAB programming environment. The performance of the algorithms is measured using average test cases generated per path (ATCP) and average percentage coverage (APC) metrics. Number of individual or population size doesn’t affect performance much but it should be between 20 and 40 but various parameters have to be tuned inorder to get good results.