对于测试用例生成的遗传算法改进

软件测试技术中，高效的测试用例生成能够大幅简化测试工作，提高测试效率，节省软件开发成本. 遗传算法作为一种高效的搜索寻优算法已被广泛应用到测试用例自动生成的研究中，然而传统的遗传算法虽然具有良好的全局搜索能力，但对于局部空间的求精问题却不是很有效，存在早熟问题. 针对这些问题，结合禁忌搜索算法，对传统的遗传算法在适应度函数、遗传算子方面进行改进，并进行遗传导向控制，能够有效控制遗传早熟问题，提高遗传算法的局部寻优能力. 实验结果表明，本文所建议的方法在测试用例生成的效率和效果方面均优于基于传统遗传算法的测试用例方法.

Improved Genetic Algorithm Used in Test Cases

In software testing process, efficient test case generation can dramatically simplify testing, improve test efficiency and save software development costs. As an effective search algorithm, genetic algorithm has been widely applied to the study on automatic generation of test cases, and has good global search capability. However, some inherent limits of this algorithm exist, such as low optimization efficiency, premature convergence, etc. This paper proposes a modified genetic algorithm combined with tabu search algorithm, improves the select and crossover operator of genetic algorithm against the shortcomings of premature convergence, and adopt the optimal preservation strategy for improving search capabilities in the local space and the overall operating efficiency. Experiments result shows that the new algorithm has obvious advantages in efficiency and effectiveness compared with traditional genetic algorithm for test case generation.