| 基于改进遗传算法的测试数据自动生成的研究 |
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| 引用本文： | 高雪笛,周丽娟,张树东,柳昊明. 基于改进遗传算法的测试数据自动生成的研究[J]. 计算机科学, 2017, 44(3): 209-214 |
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| 作者姓名： | 高雪笛 周丽娟 张树东 柳昊明 |
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| 作者单位： | 首都师范大学信息工程学院 北京100048;成像技术北京市高精尖创新中心 北京100190,首都师范大学信息工程学院 北京100048;成像技术北京市高精尖创新中心 北京100190,首都师范大学信息工程学院 北京100048;成像技术北京市高精尖创新中心 北京100190,北京航空航天大学计算机学院 北京100048 |
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| 基金项目： | 本文受国家自然科学基金(31571563),国家科技支撑计划项目(2013BAH19F01),国外访学项目(067145301400),北京市属高等学校创新团队建设与教师职业发展计划项目,高可靠嵌入式系统技术北京市工程研究中心资助 |
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| 摘 要： | 测试数据自动生成是软件测试的基础,也是测试自动化技术实现的关键环节。为了提高测试自动化的效率,在 结合 测试数据自动生成模型的基础上,提出一种 传统遗传算法的改进算法。该算法使用了自适应交叉算子和变异算子,并引入模拟退火机制对其进行改进。同时,该算法还对适应度函数进行了合理的设计,以加速数据的优化过程。通过三角形程序、折半查找和冒泡排序程序,与基本遗传算法、自适应遗传算法进行了比较与分析,并且对改进算法做了性能分析。实验结果表明了该算法的实用性以及在测试数据生成中的可行性和高效性。
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| 关 键 词： | 软件测试 遗传算法 哈明函数 测试数据自动生成 |
| 收稿时间： | 2016-01-25 |
| 修稿时间： | 2016-05-01 |
Research on Test Data Automatic Generation Based on Improved Genetic Algorithm |
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| GAO Xue-di,ZHOU Li-juan,ZHANG Shu-dong and LIU Hao-ming. Research on Test Data Automatic Generation Based on Improved Genetic Algorithm[J]. Computer Science, 2017, 44(3): 209-214 |
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| Authors: | GAO Xue-di ZHOU Li-juan ZHANG Shu-dong LIU Hao-ming |
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| Affiliation: | College of Information Engineering,Capital Normal University,Beijing 100048,China;Beijing Advanced Innovation Center for Imaging Technology,Beijing 100190,China,College of Information Engineering,Capital Normal University,Beijing 100048,China;Beijing Advanced Innovation Center for Imaging Technology,Beijing 100190,China,College of Information Engineering,Capital Normal University,Beijing 100048,China;Beijing Advanced Innovation Center for Imaging Technology,Beijing 100190,China and School of Computer Science and Engineering,Beihang University,Beijing 100048,China |
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| Abstract: | Automatic test data generation is the basis of software testing,and it is also a key link in the process of test automation technology.In order to improve the efficiency of testing automation,a new algorithm was proposed to improve the traditional genetic algorithm based on the combination of test data automatic generation system model.The adaptive crossover operator and mutation operator are used in this algorithm,and the improved simulated annealing mechanism is introduced to improve it.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 program,binary search and bubble sort program,the basic genetic algorithm and the adaptive genetic algorithm were compared,and the performance test was done for improved algorithm.Experimental results show the practicability as well as feasibility and efficiency of the algorithm in the test data generation. |
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| Keywords: | Software test Generic algorithm Hamming function Automatic test data generation |
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