摘要
利用遗传算法进行测试数据自动生成是近年来的研究热点,其有效性高度依赖于适应度函数的选取和初始种群的筛选。该文探索将遗传算法应用到IMX(Integrated Management X-software)系统测试数据自动生成以提高其回归测试的质量,将IMX系统专业测试人员手动生成的测试数据作为基础测试数据,并提出一种基于测试路径对目标路径覆盖率的初始种群筛选标准。在三角形程序和IMX系统平台上的实验表明,所提方法在寻找测试数据时所用的时间和迭代次数较少,且生成的测试数据具有较好的多样性。
Using genetic algorithms to generate test data automatically is becoming a hot topic in recent years, the method on effectively generating data is highly dependent on choosing the proper fitness function and the selecting standard. The genetic algorithm is used on Integrated Management X-software（IMX） system to help it improve the quality of regression test. Those basic test data used in this paper are taken from the data that generated by professional testers in IMX, and an initial population selecting standard is proposed based on the coverage. Experiments on IMX and triangle program show that the proposed algorithm is more effective than others, for example, with less time and iteration the method can find the testing data correctly, especially on data variety.
出处
《电子与信息学报》
EI
CSCD
北大核心
2015年第10期2501-2507,共7页
Journal of Electronics & Information Technology
基金
国际航空运输协会(IATA)项目(70003418)
国家科技支撑计划(2014BAJ04B02)~~
关键词
测试数据自动生成
遗传算法
初始种群筛选
适应度函数
IMX系统
Test data generation
Genetic algorithm
Initial population selecting
Fitness function
Integrated Management X-software（IMX） system
