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: Inertia weight, Simplified particle swarm algorithm, Test case

CLC Number: 

  • TP311
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