Computer Science ›› 2018, Vol. 45 ›› Issue (11): 199-203.doi: 10.11896/j.issn.1002-137X.2018.11.031

• Software & Database Technology • Previous Articles     Next Articles

Combinatorial Test Case Generation Method Based on Simplified Particle Swarm Optimizationwith Dynamic Adjustment

BAO Xiao-an1, BAO Chao1, JIN Yu-ting1, CHEN Chun-yu1, QIAN Jun-yan2, ZHANG Na1   

  1. (School of Information Science and Technology,Zhejiang Sci-Tech University,Hangzhou 310018,China)1
    (Guangxi Key Laboratory of Trusted Software,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China)2
  • Received:2017-10-09 Published:2019-02-25

Abstract: One of the keys for the optimized combinatorial test is that the generated test case can cover more combinations,and the particle swarm algorithm has the distinctive advantage and capability in generating strong combinatorial coverage cases.This paper proposed a combinatorial test case generation method based on simplified particle swarm optimization based on dynamic adjustment.In this method,test case is generated based on particle swarm algorithm,and the mixed priority one-test-at-a-time strategy and simplified particle swarm optimization algorithm based on dynamic adjustment are combined to generate combinatorial test case set,excluding the influence of velocity factors on the process of particle optimization.Then,a particle convergence criterion is defined,and the inertia weight is dynamically adjusted based on the premature convergence degree ofparticle swarm,so as to prevent that the particles fall into the local optimum and its convergence is slow later,thus improving the capability of coverage combination of the coverage table gene-rated by the particle swarm algorithm.Experiments show that the simplified particle swarm optimization algorithm based on dynamic adjustment has certain advantages in the aspect of case scale and time cost.

Key words: Simplified particle swarm algorithm, Test case, Inertia weight

CLC Number: 

  • TP311
[1]WANG Z Y,XU B W,NIE C H,et al.Survey of combinatorial test generation [J].Journal of Frontiers of Computer Science and Technology,2008,2(6):571-588.(in Chinese)
王子元,徐宝文,聂长海,等.组合测试用例生成技术[J].计算机科学与探索,2008,2(6):571-588.
[2]JIA J T.Research of Automatic Testcase Generation Functions Based onParticle Swarm Optimization Algorithm [J].Computer Technology and Development,2010,20(9):24-27.(in Chinese)
贾冀婷.基于粒子群算法的测试用例自动生成方法研究[J].计算机技术与发展,2010,20(9):24-27.
[3]WANG Z Y,NIE C H,XU B W,et al.Optimal Test Suite Ge- neration Methods for Neighbor Factors Combinatorial Testing [J].Chinese Journal of Computers,2007,30(2):200-211.(in Chinese)
王子元,聂长海,徐宝文,等.相邻因素组合测试用例集的最优生成方法[J].计算机学报,2007,30(2):200-211.
[4]WANG Z Y,QIAN J,CHEN L,et al.Generating Variable Strength Combinatorial Test Suite with one-test-at-a-time Stra-tegy [J].Chinese Journal of Computers,2012,35(12):2541-2552.(in Chinese)
王子元,钱巨,陈林,等.基于One-test-at-a-time策略的可变力度组合测试用例生成方法[J].计算机学报,2012,35(12):2541-2552.
[5]BAO X A,YANG Y J,ZHANG N,et al.Test Case Generation Method Based on Adaptive Particle Swarm Optimization[J].Computer Science,2017,44(6):177-181.(in Chinese)
包晓安,杨亚娟,张娜,等.基于自适应粒子群优化的组合测试用例生成方法[J].计算机科学,2017,44(6):177-181.
[6]BERGH F V D,ENGELBRECHT A P.Cooperative learning in neural networks using particle swarm optimizers[J].South African Computer Journal,2000,26:84-90.
[7]RATNAWEERA A,HALGAMUGE S K,WATSON H C.Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients[J].IEEE Transactions on Evolutionary Computation,2004,8(2):240-255.
[8]HU W,LI Z S.A Simpler and More Effective Particle Swarm Optimization Algorithm [J].Journal of Software,2007,18 (4):861-868.(in Chinese)
胡旺,李志蜀.一种更简化而高效的粒子群优化算法[J].软件学报,2007,18(4):861-868.
[9]COHEN D M,DALAL S R,FREDMAN M L,et al.The AETG System:An Approach to Testing Based on Combinatorial Design[J].IEEE Transactions on Software Engineering,1997,23(7):437-444.
[10]CHEN X,GU Q,WANG Z Y,et al.Framework of Particle Swarm Optimization Based Pairwise Testing [J].Journal of Software,2011,22(12):2879-2893.(in Chinese)
陈翔,顾庆,王子元,等.一种基于粒子群优化的成对组合测试算法框架[J].软件学报,2011,22(12):2879-2893.
[11]WILLIAMS A W.Determination of Test Configurations for Pair-Wise Interaction Coverage[C]∥International Conference on Testing Communicating Systems:TOOLS and Techniques.DBLP,2000:59-74.
[12]SHI Y,EBERHART R C.Fuzzy adaptive particle swarm optimization[C]∥Proceedings of the 2001 Congress on Evolutiona-ry Computation.IEEE Xplore,1997:101-106.
[13]EBERHART R C,SHI Y.Tracking and optimizing dynamic systems with particle swarms[C]∥Proceedings of the 2001 Congress on Evolutionary Computation,2001.IEEE,2002:94-100.
[14]YOU B,CHEN G,GUO W.A Discrete PSO-Based Fault-Tole- rant Topology Control Scheme in Wireless Sensor Networks[C]∥Advances in Computation and Intelligence-International Symposium(Isica 2010).Wuhan,China,DBLP,2010:1-12.
[15]GONG M G,JIAO L C,YANG D D,et al.Research on Evolutionary Multi-Objective Optimization Algorithms [J].Journal of Software,2009,20(2):271-289.(in Chinese)
公茂果,焦李成,杨咚咚,等.进化多目标优化算法研究[J].软件学报,2009,20(2):271-289.
[16]ZHANG N,YAO L,BAO X A,et al.Multi-Objective Optimization Based On-Line Adjustment Strategy of Test Case Prioritization[J].Journal of Software,2015,26(10):2451-2464.(in Chinese)
张娜,姚澜,包晓安,等.多目标优化的测试用例优先级在线调整策略[J].软件学报,2015,26(10):2451-2464.
[1] LIU Fang, HONG Mei, WANG Xiao, GUO Dan, YANG Zheng-hui, HUANG Xiao-dan. Performance Analysis of Randoop Automated Unit Test Generation Tool for Java [J]. Computer Science, 2020, 47(9): 24-30.
[2] JI Shun-hui, ZHANG Peng-cheng. Test Case Generation Approach for Data Flow Based on Dominance Relations [J]. Computer Science, 2020, 47(9): 40-46.
[3] ZHANG Zhi-qiang, LU Xiao-feng, SUI Lian-sheng, LI Jun-huai. Salp Swarm Algorithm with Random Inertia Weight and Differential Mutation Operator [J]. Computer Science, 2020, 47(8): 297-301.
[4] XIA Chun-yan, WANG Xing-ya, ZHANG Yan. Test Case Prioritization Based on Multi-objective Optimization [J]. Computer Science, 2020, 47(6): 38-43.
[5] FENG Shen-feng, GAO Jian-hua. Test Case Prioritization Method Based on AHP for Regression Testing [J]. Computer Science, 2019, 46(8): 233-238.
[6] HUANG Zhao,HUANG Shu-guang,DENG Zhao-kun,HUANG Hui. Automatic Vulnerability Detection and Test Cases Generation Method for Vulnerabilities Caused by SEH [J]. Computer Science, 2019, 46(7): 133-138.
[7] ZHANG Na,TENG Sai-na,WU Biao,BAO Xiao-an. Test Case Generation Method Based on Particle Swarm Optimization Algorithm [J]. Computer Science, 2019, 46(7): 146-150.
[8] ZHANG Na, XU Hai-xia, BAO Xiao-an, XU Lu, WU Biao. Multi-objective Test Case Prioritization Method Combined with Dynamic Reduction [J]. Computer Science, 2019, 46(12): 208-212.
[9] HUANG Yang, LU Hai-yan, XU Kai-bo, HU Shi-juan. S-shaped Function Based Adaptive Particle Swarm Optimization Algorithm [J]. Computer Science, 2019, 46(1): 245-250.
[10] BAO Xiao-an, XIONG Zi-jian, ZHANG Wei, WU Biao, ZHANG Na. Approach for Path-oriented Test Cases Generation Based on Improved Genetic Algorithm [J]. Computer Science, 2018, 45(8): 174-178.
[11] SUN Min CHEN, Zhong-xiong, LU Wei-rong. Task Scheduling Algorithm Based on DO-GAPSO under Cloud Environment [J]. Computer Science, 2018, 45(6A): 300-303.
[12] CHENG Jing, ZHANG Tao, WANG Tao, DONG Zhan-wei. Graphic Complexity-based Prioritizing Technique for Regression Testing of Mobile Navigation Service [J]. Computer Science, 2018, 45(6): 141-144.
[13] DONG Hong-bin, LI Dong-jin and ZHANG Xiao-ping. Particle Swarm Optimization Algorithm with Dynamically Adjusting Inertia Weight [J]. Computer Science, 2018, 45(2): 98-102.
[14] YANG Hong, HONG Mei, QU Yuan-yuan. Approach of Mutation Test Case Generation Based on Model Checking [J]. Computer Science, 2018, 45(11A): 488-493.
[15] HUANG Yu-yao, LI Feng-ying, CHANG Liang and MENG Yu. Symbolic ZBDD-based Generation Algorithm for Combinatorial Testing [J]. Computer Science, 2018, 45(1): 255-260.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] . [J]. Computer Science, 2018, 1(1): 1 .
[2] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[3] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[4] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[5] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[6] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[7] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[8] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[9] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[10] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .