Comparative Study of BBBC and PSO Algorithms for Automated Test Data Generation for Softwares

This paper compares metaheuristic search algorithms namely Particle Swarm Optimization and Big Bang Big Crunch algorithms for automated test case generation using path testing criterion. For input generation symbolic execution method has been used in which first, target path is selected from Control Flow Graph of Software under Test and then inputs are generated using search algorithms which can evaluate path predicate corresponding to the path which is evaluated true. We have experimented on two real world and bench marked programs Triangle Classifier and Line-Rectangle Classifier 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 and average percentage coverage metrics. The PSO algorithm has proved its mettle by generating test data for small as well as large domain but BBBC algorithm failed to generate data for complex path having equality constraint especially in larger domain.