Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (5): 72-81.doi: 10.11896/jsjkx.220200110

• Explainable AI • Previous Articles     Next Articles

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

ZHANG Wenning1,2,3, ZHOU Qinglei4, JIAO Chongyang1,2, XU Ting1,2,4   

  1. 1 PLA Strategic Support Force Information Engineering University,Zhengzhou 450000,China
    2 State Key Laboratory of Mathematical Engineering and Advanced Computing,Zhengzhou 450000,China
    3 Software College,Zhongyuan University of Technology,Zhengzhou 450000,China
    4 School of Information Engineering,Zhengzhou University,Zhengzhou 450000,China
  • Received:2022-02-17 Revised:2022-09-11 Online:2023-05-15 Published:2023-05-06
  • About author:ZHANG Wenning,born in 1982,Ph.D candidate,is a member of China Computer Federation.Her main research interests include intelligent computing and software engineering.
    ZHOU Qinglei,born in 1962,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include information security and software engineering.
  • Supported by:
    National Key R&D Program of China(2018********01) and Science and Technology Project in Henan Province(172102210592,212102210417).

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Abstract: 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.

Key words: Integration testing, Class integration test order, Grey wolf optimizer, Arithmetic optimization algorithm, Hybrid optimization, Random walk

CLC Number: 

  • TP311.5
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