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1. 中国科学院成都计算机应用研究所仲恺农业工程学院计算机科学与工程学院
2. 中国科学院成都计算机应用研究所
纸质出版日期:2009,
网络出版日期:2008-5-12,
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陈勇,刘勇,鲍胜利.基于伪并行遗传算法的路径测试数据自动生成[J].工程科学与技术,2009,41(5):141-145.
Chen Yong, Liu Yong, Bao Shengli. Automatic Path-Oriented Test Data Generation Using Pseudo-Parallel Genetic Algorithm[J]. Advanced Engineering Sciences, 2009,41(5):141-145.
中文摘要: 路径测试数据自动生成是结构测试中的关键问题,也是当前软件测试研究中的热点问题。为了探讨伪并行遗传算法用于路径测试数据生成的可行性及其效果,首先归纳了基于演化算法的路径测试数据自动生成方法的基本思想和流程,然后在MATLAB7.1上实现了一个基于适应度选择迁移个体并采用自由迁移策略的伪并行遗传算法和一个使用代沟的基本遗传算法。采用基于分支距离的适应度函数,以三角形分类程序为例比较了二者在生成路径测试数据时的性能差异,实验结果表明伪并行遗传算法较之基本遗传算法具有明显优势。
Abstract:Automatic path-oriented test data generation is not only a crucial problem in structural testing but a hot issue in the research area of software testing today. To investigate the feasibility of pseudo-parallel genetic algorithm’s (PPGA) application in path testing
the main idea and basic flow of automatic path-oriented test data generation using evolutionary algorithms are concluded first. Based on MATLAB7.1
a pseudo-parallel genetic algorithm and a simple genetic algorithm (SGA) using generation gap are implemented. The PPGA selects individuals for unrestricted migration based on their fitness values. Using a triangle classification program as an example
under the guidance of branch distance based fitness function
performance of generating path-oriented test data between these two algorithms are compared. Experimental results show that PPGA based approach can generate path-oriented test data more effectively and efficiently than SGA based approach does.
软件测试路径测试伪并行遗传算法测试数据生成
software testingpath testingpseudo-parallel genetic algorithmtest data generation
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