基于复杂系统遗传算法的多路径覆盖测试用例生成方法

Multiple Paths Test Case Generation Based on Complex System Genetic Algorithm

针对目前复杂系统多路径覆盖测试用例生成方法较少的问题,提出一种新的基于复杂系统的多路径覆盖测试用例生成方法。首先改进遗传算法,在种群进化中对父代选择、个体进化的学习能力和种群的自适应更新方法进行改进,以有效避免算法收敛过慢或者"早熟"现象。然后根据多路径覆盖测试的特点与要求,设计基于路径匹配的适应度函数,使得运行一次算法便可生成覆盖多条目标路径的多个测试用例。最后将该方法用于几个基准程序。实验结果表明,与已有方法比较,此方法的测试用例生成效率显著提高。

In the light of the lack of effective methods to generating test case for multiple paths coverage based on complex system,we proposed a novel evolutionary generation approach of test case for multiple paths coverage.First,generic algorithm was improved：the ability of parent evolutionary selection,individual evolution and adaptive update method of populations were improved in the evolution of population,which cound solve the algorithm for early slow convergence or premature effectively.And then,according to the features and requirements of multiple paths coverage,fitness function based on path-match was designed so that in one run,it was able to generate multiple test data to cover multiple target paths.Finally,the proposed approach was applied into several benchmark programs.The experimental results show that the proposed approach can improve the efficiency of test data generation effectively.