Automatic path-oriented test data generation is not only a crucial problem in structural testing but a hot issue in the research area of software testing today. To investigate the feasibility of pseudo-parallel genetic algorithm’s (PPGA) application in path testing, the main idea and basic flow of automatic path-oriented test data generation using evolutionary algorithms are concluded first. Based on MATLAB7.1, a pseudo-parallel genetic algorithm and a simple genetic algorithm (SGA) using generation gap are implemented. The PPGA selects individuals for unrestricted migration based on their fitness values. Using a triangle classification program as an example, under the guidance of branch distance based fitness function, performance of generating path-oriented test data between these two algorithms are compared. Experimental results show that PPGA based approach can generate path-oriented test data more effectively and efficiently than SGA based approach does.
