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
Code coverage using intelligent water drop (IWD)
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 not only a crucial problem but also a hot issue in the research area of software testing today. As a robust metaheuritstic search method in complex spaces, genetic algorithm (GA) has been used to path-oriented test data generation since 1992 and outperforms other approaches. A fitness function based on branch distance (BDBFF) and another based on normalized extended Hamming distance (SIMILARITY) are both applied in GA-based path-oriented test data generation. To compare performance of these two fitness functions, a triangle classification program was chosen as the example. Experimental results show that BDBFF-based approach can generate path-oriented test data more effectively and efficiently than SIMILARITY- based approach does.