基于搜索空间自动缩减的路径覆盖测试数据进化生成

 提出一种基于搜索空间自动缩减的路径覆盖测试数据进化生成方法,首先,确定目标路径与输入变量之间的关系,将可分目标路径分离出与部分分量相关的子路径;然后,固定被穿越子路径对应的输入分量,并缩小交叉和变异操作的范围,使种群在不断缩小的空间里寻找测试数据,以提高测试数据生成的效率;最后,将提出的方法用于基准程序的路径覆盖测试数据生成,并与传统方法和随机法比较.结果表明,本文方法在生成测试数据需要的进化代数、运行时间和成功率等指标上均具有优越性.

Evolutionary Generation of Test Data for Path Coverage Based on Automatic Reduction of Search Space

A method of evolutionarily generating test data for path coverage based on automatic reduction of search space is presented in this study.First,the relation between the target path and the input variable is determined,and the divisible target path is divided into some sub-paths related to several elements of the input variable.Then,the elements corresponding to the traversed sub-path are fixed,and the range of crossover and mutation operations is reduced,therefore the original population searches for test data in a reduced space so that the efficiency of generating test data is improved.Finally,the proposed method is applied to generate test data for covering paths of a benchmark program,and compared with the traditional and random ones.The experimental results confirm that the proposed method is advantageous in the number of generations,time consumption and success rate.