计算机科学 ›› 2023, Vol. 50 ›› Issue (5): 72-81.doi: 10.11896/jsjkx.220200110
张文宁1,2,3, 周清雷4, 焦重阳1,2, 徐婷1,2,4
ZHANG Wenning1,2,3, ZHOU Qinglei4, JIAO Chongyang1,2, XU Ting1,2,4
摘要： 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。
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| [1]HANH V L,AKIF K,TRAON Y L,et al.Selecting an Efficient OO Integration Testing Strategy:An Experimental Comparison of Actual Strategies[C]//Proceedings of the 15th European Conference on Object-Oriented Programming.Budapest,Hungary,2001:381-401. [2]JIANG S J,ZHANG M,ZHANG Y M,et al.An IntegrationTest Order Strategy to Consider Control Coupling[J].IEEE Transaction on Software Engineering,2021,47(7):1350-1367. [3]CZIBULA G,CZIBULA I G,MARIAN Z.An Effective Ap-proach for Determining the Class Integration Test Order Using Reinforcement Learning[J].Applied Soft Computing,2018,65(C):517-530. [4]ZHANG M,KEUNG J W,CHEN T Y,et al.Validating Class Integration Test Order Generation Systems with Metamorphic Testing[J].Information and Software Technology,2021,132:1-13. [5]KUNG D,GAO J,HSIA P,et al.A Test Strategy for ObjectOriented Programs[C]//Proceedings of the 19th Annual International Computer Software and Applications Conference.Wa-shington:IEEE Computer Society,1995:239-244. [6]KHARI M,SINHA A,VERDU E,et al.Performance Analysis of Six Meta-heuristic Algorithms over Automated Test Suite Generation for Path Coverage based Optimization[J].Soft Computing,2020,24(12):9143-9160. [7]MIRJALILI S,MIRJALILI S M,LEWIS A.Grey Wolf Optimizer[J].Advances in Engineering Software,2014,69(3):46-61. [8]ABUALIGAH L,DIABAT A,MIRJALILI S,et al.The Arith-metic Optimization Algorithm[J].Computer Methods in Applied Mechanics and Engineering,2021,376:1-39. [9]TAI K C,DANIELS F J.Test Order for Interclass Object-Orien-ted Software[C]//Proceedings of the 21stInternational Compu-ter Software and Applications Conference.Washington:IEEE Computer Society,1997:602-607. [10]LE T Y,JERON T,JEZEQUEL M,et al.Efficient ObjectOriented Integration and Regression Test[J].IEEE Transactions on Reliability,2000,49(1):12-25. [11]BRIAND L C,LABICHE Y,WANF Y.An Investigation ofGraph-Based Class Integration Test Order Strategies[J].IEEE Transactions on Software Engineering,2003,29(7):594-607. [12]LU Y S,MAO C Y.Method of Inter Class Test Order Determination for Object Oriented Cluster Level Testing[J].Journal of Mini-Micro ComputerSystems,2005,26(6):995-999. [13]ZHANG M,KEUNG J,XIAO Y,et al.A Heuristic Approach to Break Cycles for the Class Integration Test Order Generation[C]//IEEE 43rd Annual Computer Software and Applications Conference.Washington:IEEE Computer Society,2019:47-52. [14]ZHANG Y M,JIANG S J,ZHANG H C.An Approach forClass Integration Testing based on Dynamic Dependency Relations[J].Chinese Journal of Computers,2011,34(6):1075-1089. [15]JIANG S J,ZHANG Y M,WANG Q T,et al.Design of Class Integration Test Order Based on Coupling Measures[J].International Journal on Information,2012,15(1):331-338. [16]BRIAND L C,FENG J,LABICHE Y.Experiment with Genetic Algorithms and Coupling Measures to Devise Optimal Integration Test Orders[C]//Proceedings of the 14th International Conference on Software Engineering and Knowledge Enginee-ring.New York:ACM,2002:43-50. [17]BORNER L,PAECH B.Integration Test Order Strategies toConsider Test Focus and Simulation Effort[C]//Proceedings of the International Conference on Advances in System Testing and Validation Lifecycle.Washington:IEEE Computer Society,2009:80-85. [18]CZIBULA I G,CZIBULA G,MARIAN Z.Identifying Class Integration Test Order Using an Improved Genetic Algorithm based Approach[C]//Proceedings of International Conference on Software Technologies.Berlin:Springer Verlag,2018:163-187. [19]ZHANG Y M,JIANG S J,CHEN R Y,et al.Class Integration Testing Order Determination Method based on Particle Swarm Optimization Algorithm[J].Chinese Journal of Computers,2018,41(4):931-945. [20]ZHANG Y N,JIANG S J,ZHANG Y M.Approach for Generating Class Integration Test Sequence based on Dream Particle Swarm Optimization Algorithm[J].Computer Science,2019,46(2):159-165. [21]CABRAL R,POZO A,VERGILIO S R.A Pareto Ant Colony Algorithm Applied to the Class Integration and Test Order Problem[C]//Proceedings of the 22nd IFIP International Conference on Testing Software and Systems.Berlin:Springer Verlag,2010:16-29. [22]VERGILIO S R,POZO A,GARCIA J C,et al.Multi-objectiveOptimization Algorithms Applied to the Class Integration and Test Order Problem[J].International Journal on Software Tools for Technology Transfer,2012,14(4):461-475. [23]WANG Y,YU H,ZHU Z L.A Class Integration Test OrderMethod based on the Node Importance of Software[J].Journal of Computer Research and Development,2016,53(3):517-530. [24]ZHANG M,KEUNG J W,XIAO Y,et al.Evaluating the Effects of Similar Class Combination on Class Integration Test Order Generation[J].Information and Software Technology,2021,129:1-16. [25]MARIANI T,GUIZZO G,VERGILIO S R,et al.Grammatical Evolution for the Multi-Objective Integration and Test Order Problem[C]//Proceedings of the Genetic and Evolutionary Computation Conference.New York:ACM,2016:1069-1076. [26]FEI K X,WANG Y W,GONG Y Z.An Integrated Test Sequence Generation Method Based on Software Metrics[J].Journal of Zhengzhou University(Engineering Science),2021,42(4):1-6. [27]HEWETT R,KIJSANAYOTHIN P.Automated Test OrderGeneration for Software Component Integration Testing[C]//Proceedings of IEEE/ACM International Conference on Automated Software Engineering.Washington:IEEE Computer So-ciety,2009:211-220. [28]WANG Z S,LI B X.Using Coupling Measure Technique and Random Iterative Algorithm for Inter-class Integration Test Order Problem[C]//Proceedings of the 34th Annual IEEE Computer Software and Applications Conference.Washington:IEEE Computer Society,2010:329-334. |
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