基于传统H算法改进的回归测试用例优化算法  

The Optimized Research of Regression Test Cases Based on Improved Traditional H Algorithm

作　　者：成亚玲[1] 谭爱平[1] 彭湘华[1] 

机构地区：[1]湖南工业职业技术学院,湖南长沙410208

出　　处：《湖南工业职业技术学院学报》2015年第4期1-7,共7页Journal of Hunan Industry Polytechnic

基　　金：湖南省教育厅科研项目"基于工作流的期刊在线投稿系统的设计与实现"(项目编号:12C1033)

摘　　要：回归测试用例的优化选择是为了达到良好的回归测试覆盖率,提高回归测试效率。根据回归测试用例优化问题的性质和自身条件,针对五种经典传统启发式算法存在的不足,论述了如何改进传统H算法得到回归测试用例优化选择的局部更优解,并给出了算法的框架、程序、结构流程及具体实现。最后,通过大量算法分析和实例研究对改进后的H算法和其它算法求得的子集总代价进行对比,结果表明:新算法比传统经典算法和目前流行的一些智能算法能求得更优的解,证明了算法的可行性。Research on the optimized selection of regression test cases is to ensure the fine test coverage and the higher test efficiency. According to the nature and their own conditions of the Optimized Selection problems, aiming at the existing problems of the five kinds of classic traditional heuristic algorithm, this paper discussed how to improve the traditional H algorithm and obtain the local optimization selection of test cases. Meanwhile, the algorithm framework, code, structure and concrete realization process were given. Meanwhile, it made a comparison on the subsets of the total cost between the new improved H algorithm and the other five algorithms through a large number of algorithm analysis and cases study. The experimental results show that the proposed method can obtain the better subset than the traditional classic algorithms and some currently popular intelligent algorithm, and the feasibility of the algorithm were proved with results.

关 键 词：回归测试 测试用例优化选择 启发式算法 测试用例集约简 覆盖率 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论]