基于混合遗传算法在测试用例生成中的研究

Test Case Generation Based on Hybrid Genetic Algorithm

采用智能优化算法求解测试用例生成问题是软件测试自动化领域的研究热点。针对标准遗传算法在测试用例生成中的不足,提出基于混合遗传算法的生成方法。该算法结合分支覆盖和路径覆盖两个指标对适应度函数进行优化设计,以加快数据的优化过程。通过调节因子对自适应的交叉和变异算子进行改进,并引入模拟退火机制,提高算法的局部搜索能力。实验表明,该算法在测试用例生成中的求解质量和运行效率均优于标准遗传算法。

With intelligent optimization algorithm to solve test case generation problem is a research hotspot in the field of software test automation. Aiming at the shortcomings of standard genetic algorithm in test case generation, a hybrid genetic algorithm based generation method is proposed. The algorithm combines two indicators of branch coverage and path coverage to optimize the fitness function to speed up the data optimization process. The adaptive crossover and mutation operators are improved by the adjustment factor, and the simulated annealing mechanism is introduced to improve the local search ability of the algorithm. The experimental results show that the proposed algorithm is superior to the standard genetic algorithm in the quality and operational efficiency of test case generation.