面向多目标测试用例优先排序的蚁群算法信息素更新策略  

Pheromone updating strategy of ant colony algorithm for multi-objective test case prioritization

作　　者：邢行[1] 尚颖[1] 赵瑞莲[1] 李征[1] 

机构地区：[1]北京化工大学信息科学与技术学院,北京100029

出　　处：《计算机应用》2016年第9期2497-2502,共6页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61170082;61472025);教育部新世纪优秀人才计划项目(NCET-12-0757)~~

摘　　要：针对蚁群算法在求解多目标测试用例优先排序(MOTCP)时收敛速度缓慢、易陷入局部最优的问题,提出一种基于上位基因段(ETS)的信息素更新策略。利用测试用例序列中ETS可以决定适应度值的变化,选取ETS作为信息素更新范围,再根据ETS中测试用例间的适应度增量和测试用例的执行时间更新路径上的信息素值。为进一步提升蚁群算法求解效率、节省蚂蚁依次访问测试用例序列的时间,优化的蚁群算法还通过估算ETS长度重新设置蚂蚁遍历测试用例的搜索终点。实验结果表明,与优化前的蚁群算法及NSGA-Ⅱ相比,优化后的蚁群算法能提升求解MOTCP问题时的收敛速度,获得更优的Pareto解集。The Ant Colony Optimization （ACO） has slow convergence and is easily trapped in local optimum when solving Muhi-Objective Test Case Prioritization （ MOTCP）. Thus, a pheromone updating strategy based on Epistatic-domain Test case Segment （ETS） was proposed. In the scheme, ETS existed in the test case sequence was selected as a pheromone updating scope, because ETS can determine the fitness value. Then, according to the fitness value increment between test cases and execution time of test cases in ETS, the pheromone on the trail was updated. In order to further improve the efficiency of ACO and reduce time consumption when ants visited test cases one by one, the end of ants＇ visiting was reset by estimating the length of ETS using optimized ACO. The experimental results show that compared with the original ACO and NSGA- Ⅱ, the optimized ACO has faster convergence and obtains better Pareto optimal solution sets in MOTCP.

关 键 词：蚁群算法 信息素更新 多目标的测试用例优先排序 回归测试 上位基因段 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论]