Abstract
This paper presents a method for path testing by generating the test data automatically and optimizing the test data to test the critical paths for a software under test (SUT), using real-coded genetic algorithm. Real encoding is used for automatic test data generation, and a representative test suite, which achieves 100% path coverage, is found as an optimum result. In this paper, the proposed real-coded genetic algorithm for path coverage (RCGAPC) generates a set of inputs for testing a specific software and outperforms by giving effective and efficient results in terms of less number of test data generation counts. In the proposed approach, one-to-one injective mapping scheme is used for mapping the test data to the corresponding path and the most critical path is covered during path testing of a specific software. It seems to be faster than the traditional GA in covering critical path. The proposed method can reduce the number of test data generation required for path testing of a SUT and give an optimized Test suite that covers 100% path for specific software.
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Mishra, D.B., Mishra, R., Das, K.N., Acharya, A.A. (2019). Test Case Generation and Optimization for Critical Path Testing Using Genetic Algorithm. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 817. Springer, Singapore. https://doi.org/10.1007/978-981-13-1595-4_6
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