结合关键点概率与路径相似度的多路径覆盖策略

Multi-path Coverage Strategy Combining Key Point Probability and Path Similarity

利用多种群遗传算法解决多路径覆盖问题,是测试数据自动生成领域一个重要的研究方向.为了提高多路径覆盖测试数据自动生成的效率,提出一种将关键点概率和路径相似度相结合的多路径覆盖策略.首先,将理论路径划分成易覆盖、难覆盖及不可达路径;然后,通过易覆盖路径统计关键点概率,依此概率计算个体对生成测试数据的贡献度,并利用贡献度改进适应度函数,同时根据关键点概率对目标路径进行排序;最后,使用多种群遗传算法生成覆盖目标路径的测试数据,在进化过程中,子种群覆盖当前目标路径后,继续尝试覆盖该目标路径的相似路径.实验结果表明,该方法能够有效地提高多路径覆盖测试数据生成的效率.

Using multi-population genetic algorithm to solve the problem of multi-path coverage is an important research direction in the field of automatic generation of test data.In order to improve the efficiency of multi-path coverage test data automatic generation,a multi-path coverage strategy combining key point probability and path similarity is proposed.Firstly,the theoretical path is divided into easily-covered,difficultly-covered,and unreachable paths.Then,the key point probability is counted through the easily-covered paths,the contribution of the individual to the generated test data is calculated by using this probability,and the contribution isusedto improve the fitness function,at the same time,the target path is sorted according to the key point probability.Finally,the test data covering the target path is generated by using multi-population genetic algorithm.After the sub-population covers the current target path during the evolution process,it continues to try to cover similar paths of the target path.The experimental results show that the proposed method can improve the efficiency of multi-path coverage to generate test data.