基于自适应遗传算法的软件测试用例自动生成

在软件测试中,测试成功的关键是快速、高效的生成测试用例.遗传算法是一种通过模拟自然界生物进化过程搜寻最优解的一种算法,算法通过选择、交叉和变异操作引导算法搜索方向,逐步接近全局最优解.传统遗传算法由于具有较好的全局搜索能力,因此被很多科研人员应用于测试用例生成.但遗传算法的固有缺陷"早熟收敛",容易导致算法收敛于局部最优.针对这种情况,提出一种自适应遗传算法,该算法交叉算子和变异算子可根据程序变化自动调整,随后,将改进后的算法应用于一程序的测试用例生成中.测试结果表明该算法在测试用例生成的效率和效果方面优于传统搜索算法和普通改进算法.

Automatic Testing-Case Generation Based on Adaptive Genetic Algorithm

In software testing, the key to a successful test is a fast and efficient testing-case generation. Genetic algorithm is an algorithm to search for the optimal solution by simulating the natural process of evolution. The algorithm guides the of search direction through the selection, crossover and mutation operations. to reach the global optimal solution step by step. Traditional genetic algorithm is widely used in the test case generation by many scientific researchers due to its better global search ability. But the genetic algorithm can easily lead to convergence to a local optimal solution because of its inherent defects "premature convergence". In order to solve this problem, the author proposed an adaptive genetic algorithm in this paper. The crossover operator and mutation operator of the proposed algoritym can be adjusted automatically according to the change of the program. The improved algorithm is then applied in the test case generation process. The test results show that this algorithm is better than the traditional search algorithm and common improved algorithm in efficiency and effectiveness of testing-case generation.