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Automatic Path-Oriented Test Data Generation Using a Multi-population Genetic Algorithm

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2 Author(s)
Yong Chen ; Chengdu Inst. of Comput. Applic., Chinese Acad. of Sci., Chengdu ; Yong Zhong

Automatic path-oriented test data generation is an undecidable problem and genetic algorithm (GA) has been used to test data generation since 1992. In favor of MATLAB, a multi-population genetic algorithm (MPGA) was implemented, which selects individuals for free migration based on their fitness values. Applying MPGA to generating path-oriented test data generation is a new and meaningful attempt. After depicting how to transform path-oriented test data generation into an optimization problem, basic process flow of path-oriented test data generation using GA was presented. Using a triangle classifier as program under test, experimental results show that MPGA based approach can generate path-oriented test data more effectively and efficiently than simple GA based approach does.

Published in:

Natural Computation, 2008. ICNC '08. Fourth International Conference on  (Volume:1 )

Date of Conference:

18-20 Oct. 2008