基于蚁群算法优化软件测试策略

摘要

It is an essential issue to improve the fault detecting ability and reduce the testing cost of software testings in the study of software testing optimization. Based on Markov decision model for software testing, targeting at reducing the software testing cost and improving the fault detection capability of testing, the paper makes use of the ant colony algorithm to offer a learning strategy for optimizing the testing profile, and applies the acquired optimal testing profile to optimizing software tests. Experiment results show that the learning strategy that uses the ant colony algorithm is far better than the random testing strategy with respect to significantly reducing the testing cost and improving the fault detecting capability. It is an important supplementary for heuristic methods of software testing optimization.%提高软件测试的缺陷检测能力,有效降低测试成本是软件测试优化研究中的关键问题.基于软件测试的Markov决策模型,以降低软件测试成本,提高测试的缺陷检测能力为目标,运用蚁群算法给出一种优化测试剖面的学习策略,将所得到的最优测试剖面用于优化软件测试.实验结果表明运用蚁群算法的学习策略要远优于随机测试策略,能显著降低测试成本和提高缺陷检测能力,是软件测试优化启发式方法的一个重要补充.