This paper discusses an approach to generate test data for path coverage based testing using Genetic Algorithms, Differential Evolution and Artificial Bee Colony optimization algorithms. Control flow graph and cyclomatic complexity of the example program has been used to find out the number of feasible paths present in the program and it is compared with the actual no of paths covered by the evolved test cases using those meta-heuristic algorithms. Genetic Algorithms, Artificial Bee Colony optimization and Differential Evolution are acting here as meta-heuristic search paradisms for path coverage based test data generation. Finally the performance of the test data generation using three meta-heuristic optimization algorithms are empirically evaluated and compared.
An evaluation of Differential Evolution in software test data generation
