ASID'09 Proceedings of the 3rd international conference on Anti-Counterfeiting, security, and identification in communication
Experimental study on GA-based path-oriented test data generation using branch distance
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Grouping target paths for evolutionary generation of test data in parallel
Journal of Systems and Software
Heuristic search-based approach for automated test data generation: a survey
International Journal of Bio-Inspired Computation
Dynamic stopping criteria for search-based test data generation for path testing
Information and Software Technology
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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.