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A Novel Approach for Test Data Generation
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Software testing is an essential phase in software design process, accounting for more than half of the total cost due to its rigorous and time-consuming nature. Path test data generation is the most important stage in software testing, and researchers have devised several methods to automate it. In this research, a novel approach based on ant colony optimization and negative selection algorithm (NSA) is projected to automatically create test data for path testing. The most widely used benchmark programs such as triangle classification, dayfinder, minmax and isprime, has been used to test the proposed approach. When compared to random testing, the experimental findings reveal that the proposed method is more efficient in terms of coverage, execution time and more effective in terms of test data creation.
Keywords
Test Data Generation (TDG), Meta-Heuristic, Artificial Immune Algorithm, ACO, NSA, Path Coverage, Fitness Function
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