Computer Science ›› 2016, Vol. 43 ›› Issue (11): 252-256, 279.doi: 10.11896/j.issn.1002-137X.2016.11.049

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String-type Test Data Generation Based on Mutation Particle Swarm Optimization

LI Gang, YU Lei, SUN Hui-hui, ZHANG Xing-long and HOU Shao-fan   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Search-based algorithm is widely used in test data generation for path coverage.However,current methods are not efficient enough dealing with string-type test data.In order to generate string-type test data efficiently,a novel approach based on mutation particle swarm algorithm (MPSO) was presented.In this approach,once a swarm is randomly generated,PSO is used to evolve the swarm by drawing the whole swarm towards the optimal particle,and mutation operation is carried out with certain probability to avoid falling into local optimal.For the sake of providing effective guidance for the search process,this paper improved the classical fitness function by modifying the calculation of branch distance to make it applicable for string-type test data.The experimental results show that our proposed method not only has high success rate and stability,but also can improve the efficiency of test data generation.

Key words: Test data generation,String-type test data,Branch distance,MPSO

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