基于遗传算法的数据流测试用例自适应生成算法  

Data Flow Test-Cases Adaptive Generation Algorithm Based on Genetic Algorithm

作　　者：许力[1] 陈江勇[1] 

机构地区：[1]福建师范大学网络安全与密码技术重点实验室,福州350007

出　　处：《计算机系统应用》2013年第7期90-94,共5页Computer Systems & Applications

基　　金：福建省高校产学合作科技重大项目(2011H6008);福建省自然科学基金(2011J5148)

摘　　要：测试用例的设计是软件测试实施的首要环节,对后期测试工作具有重要的指导作用,也是提高质量软件的根本保证.针对Moheb R.Girgis算法的不足,通过引入分支函数和改进遗传算法中的自适应性,提出一种改进的数据流测试用例的自动生成算法,实验表明,改进算法在收敛速度和覆盖率等关键性能上都有较明显提高.The design of test-cases is one of the most important parts of software testing, which play an important role in guiding the post-testing and also is the fundamental guarantee of quality software. For the shortcoming of method raised by Moheb R. Girgis, an improved genetic algorithm for the automatic generation of data flow test-cases was proposed by introducing the branch functions and adaptive genetic strategies. Experiments show that the improved algorithm has a more increase in the performance of convergence rate and coverage rate.

关 键 词：遗传算法 数据流 测试用例 自动化测试 

分 类 号：TP311.52[自动化与计算机技术—计算机软件与理论]