【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。更多还原

【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.更多还原