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Automatic Test Data Generation Based on a Modified Genetic Algorithm
دوره 1، شماره 3، 2019، صفحات 136 - 143
1 Faculty of Computer Engineering K. N. Toosi University Tehran, Iran
چکیده :
Software Testing is one of the essential parts of the software development lifecycle and structural testing is one of the most widely used testing principles to test various software. In the structural test, the test data generation is very important. Therefore, the problem becomes a search problem and Search Algorithms can be used. Genetic Algorithm(GA) is one of the widely used algorithms in this field. For the problem that GA suffers from large iteration times and low efficiency in test data generation, this paper proposes a Modified Genetic Algorithm(MGA), in this method, we design the chromosome probability of crossover and mutation which has a relationship with chromosome adaptability. The experimental result shows that MGA has faster convergence speed and higher test data generation efficiency compared with traditional GA.
Software Testing is one of the essential parts of the software development lifecycle and structural testing is one of the most widely used testing principles to test various software. In the structural test, the test data generation is very important. Therefore, the problem becomes a search problem and Search Algorithms can be used. Genetic Algorithm(GA) is one of the widely used algorithms in this field. For the problem that GA suffers from large iteration times and low efficiency in test data generation, this paper proposes a Modified Genetic Algorithm(MGA), in this method, we design the chromosome probability of crossover and mutation which has a relationship with chromosome adaptability. The experimental result shows that MGA has faster convergence speed and higher test data generation efficiency compared with traditional GA.
کلمات کلیدی :
Software Testing, Test Data Generation, Search Algorithms, Genetic Algorithm.
Software Testing, Test Data Generation, Search Algorithms, Genetic Algorithm.