摘要
在软件测试中,为了更有效地生成测试用例,提出了一种改进的乌鸦搜索算法应用于软件测试中生成不同的测试用例。该算法采用柯西变异算子来自动生成具有较高变异的测试数据集,利用相对误差作为适应度函数来选择较好的测试用例。柯西变异算子的引入可以防止算法陷入局部最优,进而增强了算法搜索的效率。实验结果表明,与其他启发式算法相比,该算法在测试用例变异方面具有更好的性能。
In software testing,an improved crow search algorithm which generates different test cases in software testing is proposed to generate test cases more effectively.In this algorithm,Cauchy mutation operator is used to automatically generate test data sets with high mutation,and the relative error is used to help fitness function to select good test cases.The Cauchy mutation operator can prevent the algorithm from trapping into local optimum and enhance the search efficiency of the algorithm.Experimental results show that,compared with other meta-heuristic algorithms,the proposed algorithm has better performance in test case with mutation.
作者
李清霞
LI Qingxia(College of Computer and Information,City College of Dongguan University of Technology,Dongguan 523419,China)
出处
《应用科技》
CAS
2021年第2期69-73,共5页
Applied Science and Technology
基金
国家科技创新2030重大项目“新一代人工智能”(2018AAA0101301)
广东省普通高校特色创新项目(2018KTSCX314).
关键词
软件测试
乌鸦搜索
柯西变异
变异敏感度
感知概率
收敛性
适应度
基准程序
software testing
crow search
Cauchy mutation
mutation sensitivity
perception probability
convergence
fitness
benchmark function
