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Genetic Algorithm-Based Test Data Generation for Multiple Paths via Individual Sharing

机译:基于遗传算法的个体共享多路径测试数据生成

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摘要

The application of genetic algorithms in automatically generating test data has aroused broad concerns and obtained delightful achievements in recent years. However, the efficiency of genetic algorithm-based test data generation for path testing needs to be further improved. In this paper, we establish a mathematical model of generating test data for multiple paths coverage. Then, a multipopulation genetic algorithm with individual sharing is presented to solve the established model. We not only analyzed the performance of the proposed method theoretically, but also applied it to various programs under test. The experimental results show that the proposed method can improve the efficiency of generating test data for many paths' coverage significantly.
机译:近年来,遗传算法在自动生成测试数据中的应用引起了广泛的关注,并取得了可喜的成就。但是,需要进一步提高基于遗传算法的路径测试测试数据生成的效率。在本文中,我们建立了用于生成多路径覆盖范围的测试数据的数学模型。然后,提出了一种具有个体共享性的多种群遗传算法来求解所建立的模型。我们不仅从理论上分析了该方法的性能,而且将其应用于各种测试中的程序。实验结果表明,该方法可以显着提高多路径覆盖范围的测试数据生成效率。

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