Amirhossein Damia - Faculty of Computer Engineering K. N. Toosi University Tehran, Iran
Hamid Tahermanesh - Faculy of Elelctrical Engineering K. N. Toosi University Tehran, Iran
Nasib Damia - Master of Exercise Physiology, Islamic Azad University, Yasuj Branch
Saeed Peghan - Master of MBA, Human Resources, Islamic Azad University, Dehaghan Branch

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 relationship with chromosome adaptability. Experimental result shows that MGA has faster convergence speed and higher test data generation efficiency compared with traditional GA.

Software Testing, Test Data Generation, Search Algorithms, Genetic Algorithm