基于SA-PSO的多态路径测试数据生成方法

目前测试数据生成方法多数未考虑到面向对象软件的多态特性，无法运用生成的测试数据对程序的多态信息进行充分的测试。根据多态路径测试数据生成的要求,提出了一种应用模拟退火—粒子群优化(simulated annealing-particle swarm optimization，SA-PSO)混合算法在多态路径测试中生成测试数据的方法,并通过多态性实例对基本粒子群算法、遗传算法、PSO-GA（particle swarm optimization-genetic algorithm）和SA-PSO算法在相同条件

Method of generating test data in polymorphism path based on SA-PSO

At the present, most methods of generating test data do not consider the polymorphism features of object-oriented software, which cannot use the test data to do sufficient testing for polymorphism information of the programs. According to the requirement of generating polymorphism path test data, this paper proposed a method which was used to generate test data in polymorphism path testing by using SA-PSO. In addition, some cases have been done to make comparisons among other optimization algorithms such as basic particle swarm algorithm, genetic algorithm and PSO-GA algorithm. Experiments show that SA-PSO can find out the global optimal solution more quickly with stronger search capabilities. It is proved that SA-PSO algorithm has better performance to generate test data for the test path with polymorphism information.