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Automated Test Data Generation for Coupling Based Integration Testing of Object Oriented Programs Using Particle Swarm Optimization (PSO)

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 238))

Abstract

Automated test data generation is a challenging problem for researchers in the area of software testing. Up until now, most of the work on test data generation is at unit level. Until level test data generation involves the execution of test path at unit level where interaction with other components is minimum. Test data generation for unit testing involves a single path and there is no usage of formal and actual parameters. The problem of automated test data generation becomes very challenging when we move to other levels of testing including integration testing and system level testing. At integration level, the variables are passed as arguments to other components and variables change their names; also multiple paths are executed from different components to ensure proper functionality. Recently evolutionary approaches have been proven a powerful tool for test data generation. In this paper, we have proposed a novel approach for test data generation for coupling based integration testing using particle swarm optimization. Up until now, there is no research for test data generation for coupling based integration testing using particle swarm optimization. Our approach takes the coupling path as input, containing different sub paths, and generates the test data using particle swarm optimization. We have also proposed architecture of tool for automation of our approach. In future, we will implement our proposed approach and will perform different experiments to prove its significance.

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Correspondence to Shaukat Ali Khan .

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Khan, S.A., Nadeem, A. (2014). Automated Test Data Generation for Coupling Based Integration Testing of Object Oriented Programs Using Particle Swarm Optimization (PSO). In: Pan, JS., Krömer, P., Snášel, V. (eds) Genetic and Evolutionary Computing. Advances in Intelligent Systems and Computing, vol 238. Springer, Cham. https://doi.org/10.1007/978-3-319-01796-9_12

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  • DOI: https://doi.org/10.1007/978-3-319-01796-9_12

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-01795-2

  • Online ISBN: 978-3-319-01796-9

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