基于混沌遗传算法的测试用例自动生成研究  

Test Cases Automatic Generation Based on Chaotic Genetic Algorithm

作　　者：黄陈辉 吴海涛[1] 阮江涛 钱程 HUANG Chenhui;WU Haitao;RUAN Jiangtao;QIAN Cheng(College of Information,Mechanical and Electrical Engineering,Shanghai Normal University,Shanghai 200234)

机构地区：[1]上海师范大学信息与机电工程学院,上海200234

出　　处：《计算机与数字工程》2021年第1期31-35,共5页Computer & Digital Engineering

摘　　要：测试用例自动生成是提高软件测试效率的重要手段。针对传统遗传算法的测试用例自动生成方法存在早熟收敛、迭代后期种群多样性降低等问题,提出了一种基于混沌遗传算法的测试用例自动生成模型,运用反向学习策略初始化种群,结合层接近度改进个体适应度的评价方法,并利用混沌序列优化遗传算法的交叉、变异操作。实验结果表明,与已有测试用例自动生成方法做对比,该方法提高了目标路径覆盖率、算法的效率,同时提升了测试用例生成上的全局寻优能力。Automatic generation of test cases is an important means to improve the efficiency of software testing.The automatic generation method of test cases for traditional genetic algorithm has the problems of premature convergence and population diversity reduction in the late iteration.This paper proposes an automatic generation model of test cases based on chaotic genetic algorithm.The reverse learning strategy is used to initialize the population,and the binding layer is close.The evaluation method of individual fitness is improved,and chaotic sequence is used to optimize the crossover and mutation operations of genetic algorithm.The experi⁃mental results show that compared with the existing automatic generation method of test cases,the method improves the target path coverage and the efficiency of the algorithm,and improves the global optimization ability of test case generation.

关 键 词：测试用例自动生成 遗传算法 混沌优化算法 适应度函数 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]