基于灰狼算术混合优化算法的类集成测试序列生成方法

Hybrid Algorithm of Grey Wolf Optimizer and Arithmetic Optimization Algorithm for Class Integration Test Order Generation

集成测试是软件测试的重要环节,如何决定类的集成顺序是面向对象集成测试难解决的问题之一。已有研究成果证实了基于搜索的类集成测试序列生成方法的有效性,但存在收敛速度慢、寻优精度低的问题。灰狼优化算法(Grey Wolf Optimizer, GWO)中狼群易聚集在相近的区域,易早熟收敛。算术优化算法(Arithmetic Optimization Algorithm, AOA)是新近提出的元启发式优化算法,具有良好的随机性及分散性。为此,提出了一种灰狼优化算法和算术优化算法的混合优化算法(GWO-AOA)。GWO-AOA保留GWO的位置更新策略,选用群体领导层的中心个体替换AOA的引导个体,以平衡算法的全局探索和局部开发能力,进一步引入随机游动的精英变异机制,提高算法整体的寻优精度。实验结果表明,GWO-AOA相比同类方法能用较短的时间生成测试桩代价较低的类集成测试序列,收敛速度较快。

Integration testing is an essential and important part in software testing.Determining the orders in which classes should be tested during the object oriented integration testing is a very complex problem.The search based approaches have been proved to be efficient in generating class integration test orders(CITO),with the disadvantage of slow convergence speed and low optimization accuracy.In the grey wolf optimizer(GWO)algorithm,wolves are likely to be located in the same or certain regions,thus easily being trapped into local optima.Arithmetic optimization algorithm(AOA)is a new meta heuristic technique with excellent randomness and dispersibility.To improve the performance of CITO generation,a hybrid optimization algorithm of GWO and AOA(GWO-AOA)is proposed,combining the rapid convergence speed of GWO and strong ability to avert local optima stagnation of AOA.In the GWO-AOA,the main hunting steps of GWO is unchanged and the leading individual of AOA is replaced by the center of dominant wolfs,providing a proper balance between exploration and exploitation.In addition,random walk scheme is adopted based on the random local mutation to improve the global search ability.Experimental results indicate that the proposed method can generate promising class integration test orders with less time compared to other comparative methods.