基于改进混合蛙跳算法的测试数据自动生成

A utomatic generation of test data based on improved shuffled frog leaping algorithm

测试数据的生成是实现软件测试自动化的重要环节。为了提高单元测试中测试数据的生成质量和效率,提出一种基于混合蛙跳算法的测试数据生成算法。该算法通过引入动态阈值来控制个体的移动步长,以平衡算法的全局开发和局部搜索能力,同时改进个体的随机跳动策略,转化为向随机个体学习,增强种群之间的信息交流以提高算法的全局搜索能力。将改进的算法应用到测试数据生成中。实验结果表明,在种群规模不断变化的情况下,改进的混合蛙跳算法相较于标准混合蛙跳算法、布谷鸟搜索算法、粒子群优化算法,其稳定性最强;在测试数据生成的平均迭代次数评价指标上改进的混合蛙跳算法优于对比算法。

The generation of test data is an important part of achieving software test automation.In order to improve the quality and efficiency of test data generation in unit testing,a test data generation algorithm based on shuffled frog leaping algorithm is proposed.The algorithm introduces a dynamic threshold to control the moving step size of individuals,so as to balance the global exploration and local exploitation abilities.At the same time,the worst individual random jump strategy in the standard algorithm is transformed into learning from random individual to enhance the information exchange between populations to improve the algorithm's global search capability.Apply the improved algorithm to test data generation.The experimental results show that the improved shuffled frog leaping algorithm is more stable than the standard shuffled frog leaping algorithm,cuckoo search algorithm and particle swarm optimization algorithm under the condition of changing population size.The improved shuffled frog leaping algorithm is better than the comparison algorithm in the evaluation index of the average number of iterations generated by the test data.