一种基于层接近度和分支距离函数的最优个体选择算法OA北大核心CHSSCDCSSCICSTPCD
An Optimal Individual Selection Algorithm Based on Layer Proximity and Branch Distance Function
文章针对目前最优个体评价及选择时存在的问题,在综合分析层接近度和分支距离函数特点的基础上,提出一种层接近度和分支距离函数联构的测试用例评价算法.该算法的基本思路是,在进化过程中选择领航个体时,先选择实际执行路径与目标路径接近度高的个体,再在这些个体集中选择分支距离最小的个体,从而得到领航能力最优的个体.实验结果表明,所提算法能快速寻优到测试用例,尤其是针对多层嵌套程序的测试用例,生成表现良好.
Aiming at the problems existing in the evaluation and selection of the optimal individual at present,and based on the comprehensive analysis of the characteristics of layer proximity and branch distance function,this paper proposes a test case evaluation algorithm with layer proximity and branch distance function combined.The basic idea of this algorithm is to select the individuals with high proximity between the actual execution path and the target path,and then select the individuals with the smallest branch distance in these individuals,so as to obtain the individuals with the best navigation ability.Experiment results show that the proposed algorithm can be used to quickly find test cases,especially for the test case generation of multi-layer nest-ed programs.
安迎建
上海建设管理职业技术学院 智能建筑工程学院,上海 201702
层接近度函数分支距离函数启发式方法最优个体生成算法
layer proximity functionbranch distance functionheuristic methodoptimal individual generation algorithm
《统计与决策》 2024 (012)
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