基于IAG-ABC算法的路径覆盖测试用例生成技术  

Path Coverage Test Case Generation Technology Based on IAG-ABC Algorithm

作　　者：张娜[1] 张唯 徐璐 吴彪 包晓安[1] Zhang Na;Zhang Wei;Xu Lu;Wu Biao;Bao Xiaoan(Department of Information & Electronics,Zhejiang Sci-Tech University,Hangzhou 310018,China;Graduate School of East Asian Studies,Yamaguchi University,Yamaguchi-shi 753-8514,Japan)

机构地区：[1]浙江理工大学信息学院,杭州310018 [2]山口大学东亚研究科,日本山口753-8514

出　　处：《计算机测量与控制》2019年第6期190-193,共4页Computer Measurement &Control

基　　金：国家自然科学基金项目(61502430,61562015);广西自然科学重点基金项目(2015GXNSFDA139038);浙江理工大学521人才培养计划项目

摘　　要：针对遗传算法(genetic algorithm,GA)存在搜索初期收敛速度过快、易陷入局部最优解、未能充分结合搜索过程中的反馈信息,同时人工蜂群(artificial bee colony,ABC)算法存在初期寻优速度缓慢、局部搜索具有很大随机性等问题,对遗传算法和人工蜂群算法分别进行了改进,并将改进后的两种算法进行融合,实现两者的优势互补,提出了一种自适应遗传-人工蜂群(improved adaptive genetic-artificial bee colony,IAG-ABC)算法;采用路径覆盖信息设计引导算法搜索方向的适应度函数,并用IAG-ABC算法实现路径覆盖的测试用例生成,实验结果表明,相对于标准遗传算法和已有的自适应遗传算法,IAG-ABC算法在测试用例生成效率和路径覆盖率上均有一定的优势。The genetic algorithm(GA)has the issue of premature convergence,failing to make full use of feedback information and easy to fall into local optimum.At the same time,the artificial bee colony(ABC)algorithm has slow initial optimization speed and randomness local searching during the running time.This paper improves the genetic algorithm and artificial bee colony algorithm respectively.And the two improved algorithm are combined to propose an improved adaptive genetic-artificial bee colony(IAG-ABC)algorithm in order to realize the complementary advantages between the two algorithms.According to the approach level and branch distance to design fitness function and using the IAG-ABC algorithm to solve the test cases generation problem based on path coverage.The experimental results show that the IAG-ABC algorithm has advantages about test case generation speed and path coverage rate when compare with GA and IAGA algorithm.

关 键 词：遗传算法 人工蜂群算法 路径覆盖 测试用例生成 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]