摘要
测试用例的自动生成是实现测试自动化技术的核心环节,为了提高自动化生成测试用例的效率,提出了一种基于布谷鸟搜索的改进算法.该算法采用混沌立方映射初始化种群来增加种群的多样性,并对局部最优值引入高斯扰动增加种群变化活力,加快收敛速度.另外,该算法还以分支函数插桩的方式构造适应度函数,以加快测试数据的优化.通过三个不同的开源测试程序,和基本布谷鸟算法、基本遗传算法进行实验分析.最终实验说明,该算法在自动化生成测试用例的效果和效率两方面均优于其他两种算法.
Automatic test data generation is a key link in the process of test automatic technology. In order to improve the efficiency of testing automation, a new algorithm was proposed to improve the traditional cuckoo search. Chaotic sequence generated by cube map was used to initiate individual position,which strengthened the diversity of global searching. And a Gaussian disturbance would be given on the local optimum of each generation, which could increase the vitality of population change and accelerate the convergence rate. At the same time, the algorithm is also designed to fit the fitness function to accelerate the optimization process of the data. Through the triangle, mid and equal program, the basic cuckoo search and the basic genetic algorithm were compared. Experiments result shows that the new algorithm has obvious advantages in efficiency and effectiveness compared with the other two for test case generation.
出处
《微电子学与计算机》
CSCD
北大核心
2018年第3期4-8,13,共6页
Microelectronics & Computer
基金
教育部人文社会科学青年基金项目(16YJCZH014)
关键词
软件测试
布谷鸟算法
混沌映射
高斯扰动
测试用例生成
software testing
cuckoo search
chaos mapping
gaussian disturbance
test case generation
