Automatic structural test data generation using immune genetic algorithm

Automatic structural test data generation is a key problem in software testing, which is the most important quality assurance measure currently. Its implementation can not only significantly improve the effectiveness and efficiency but also reduce the high cost of software testing. As a robust metaheuritstic search method in complex spaces, genetic algorithm (GA) has been applied to test data generation since 1992. Although GA-based test data generation outperforms other approaches, there are still several shortcomings such as slow convergence, time-consuming fitness calculation, population degeneration, and so on. In order to make better performances, this paper proposes a framework for automatic structural test data generation using immune genetic algorithm that can help to decrease probability of population degeneration and to accelerate convergence to the global optimum.