Abstract
The design of test case automatic generation technology is an important step in the implementation of software automated testing. It plays an important role in guiding the later testing work and is the fundamental guarantee for improving software quality and reliability. Ant colony optimization (ACA), as a robust optimization algorithm, has a strong ability of global optimization. In this paper, an automatic test case generation method of parallel multi-group adaptive ant colony algorithm is presented. This algorithm adopts multi-group parallel search, adopts adaptive migration rule based on population diversity, and updates pheromone strategy adaptively according to fitness function in population. Theoretical analysis and simulation experiments show that the application of this algorithm is superior to the generation of test cases.
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