基于自适应分组的大规模路径覆盖测试数据进化生成

Evolutionary generation of test data for many paths coverage based on adaptive grouping

复杂软件大规模路径覆盖测试数据生成问题普遍存在,但缺乏有效的解决方法,为此提出一种基于自适应分组的大规模路径覆盖测试数据进化生成方法.在进化过程中,通过合并满足条件的组,将测试数据生成问题转化为数量不断减少的约束多目标优化问题,采用多种群遗传算法加以解决,并给出了合并后的种群形成策略.将所提出的方法应用于基准测试程序,结果表明可以大大减少测试数据生成时间,为提高软件测试效率提供了一条可行途径.

Complicated software often contains many paths,and there is few effective method of generating test data to cover these paths up to present.Therefore,a method of evolutionary generation of test data for many paths coverage based on adaptive grouping is presented.During the process of evolution,the groups satisfying given conditions are merged based on the similarity.Then the problem of generating test data is transformed into multi-objective optimization problems with constraints whose number decreases gradually.A multi-population genetic algorithm is employed to solve the above problems,especially,the strategy of forming new populations after merging some groups is presented.The proposed method is applied to one benchmark program,and the experimental results show that,the method can decrease the time spent in generating test data greatly,and provides a feasible approach to improve the efficiency of software testing.