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In this paper, we investigate the generation of path-covered test data for automated software testing. Testing plays the critical role in detecting bugs and ensure the quality of the software in the software development lifecycle. As an alternative to the manual testing of high cost, low efficiency and poor reliability, search based approaches have been applied in automated test data generation. We propose a hybrid algorithm to generate the test data by integrating the heuristic approaches of tabu, annealing, and genetic algorithm. We discuss the effects of parameters in the process of genetic operations. Several benchmark source code pieces are used to demonstrate the effectiveness of the proposed approach. The experiment results show that the proposed algorithm has lower time complexity and better performance in convergence compared with other existing algorithms.
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