基于SA的改进遗传算法的测试数据生成研究

Study on Automatic Test Data Generation of Improved GA Based on SA

测试数据的自动生成研究是软件测试的一个焦点问题,测试数据的自动生成可以提高测试工作效率,节约测试成本。考虑遗传算法(GA)和模拟退火算法(SA)各自优缺点,提出遗传/模拟退火(GASA)混合算法的策略,在标准的GA中融入SA,在GA的局部搜索中引入SA,SA的随机状态受限于遗传优化算法的结果,GA的种群更新是由SA的退温算法和随机状态产生函数来控制,从而得到最优解。GASA算法取长补短,提高了算法的全局和局部搜索能力,能避免GA过早收敛,提高了算法搜索最优解的能力。实验结果表明,GASA算法寻找最优解所需的迭代次数明显优于标准GA。

Automatic generation technology of test data is a focus issue of software testing.It can improve test efficiency and save test costs.Considering the advantages and disadvantages of the genetic algorithm（GA） and simulated annealing（SA）,a GASA hybrid algorithm is proposed,the SA is blended into the standard GA,SA is introduced in the local GA,the random state of SA is limited by the results of the GA.GA population is updated by temperature algorithm of the SA and random state produce function,and the optimal solution is gotten. GASA can learn from each other,the global and local search ability of the algorithm is improved,the premature convergence of GA is avoided,and the ability to search the optimal solution is improved.Experimental results indicate that iterations of GASA algorithm are better than standard GA in searching for the optimal solution.