基于粒子群优化算法的测试用例生成方法  

Test Case Generation Method Based on Particle Swarm Optimization Algorithm

作　　者：张娜[1] 滕赛娜 吴彪 包晓安[1] ZHANG Na;TENG Sai-na;WU Biao;BAO Xiao-an(School of Information Science and Technology,Zhejiang Sci-tech University,Hangzhou 310018,China;The Graduate School of East Asian Studies,Yamaguchi University,Yamaguchi-shi 753-8514,Japan)

机构地区：[1]浙江理工大学信息学院,杭州310018 [2]山口大学东亚研究科,山口753-8514

出　　处：《计算机科学》2019年第7期146-150,共5页Computer Science

基　　金：国家自然科学基金(61502430,61562015);广西自然科学重点基金(2015GXNSFDA139038);浙江理工大学521人才培养计划项目资助

摘　　要：针对标准粒子群算法(Particle Swarm Optimization,PSO)中存在的早熟收敛、易于陷入局部极值的问题,提出了一种基于反向学习与再次搜索的粒子群优化算法(Reverse-Learning and Search-Again PSO,RSAPSO)用于测试用例生成。首先,通过非线性递减的惯性权重函数对学习因子进行改进,实现对种群的初步搜索,并采用梯度下降法完成对最优解与次优解的再次搜索;其次,以极值点为中心设定禁忌区域,对禁忌区域外的粒子进行反向学习,改善种群多样性;最后,采用分支距离法构造适应度函数,评判测试用例的优劣程度。实验结果表明,提出的改进方法在覆盖率、迭代次数和缺陷检测率指标上均有优势。In order to solve the problem of premature convergence and being easy to fall into local extremum in standard particle swarm optimization,this paper put forward a particle swarm optimization based on reverse-learning and search-again for test case generation.Firstly,the learning factor is improved by the nonlinear decreasing inertia weight function,realizing the preliminary search for the population,and the gradient descent method is used to complete the search-again of the optimal solution and the suboptimal solution.Secondly,setting taboo areas with extreme points as the center,the population diversity is improved by the reverse learning of the particles outside the taboo region.Finally,the branch distance method is used to construct fitness function to evaluate the quality of test cases.Experiment results show that the proposed method has advantages in coverage,iteration times and defect detection rate.

关 键 词：粒子群算法 学习因子 反向学习 再次搜索 测试用例生成 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]