一种面向路径的测试用例自动生成方法  

A PATH-ORIENTED AUTOMATIC TEST CASES GENERATION METHOD

作　　者：郑超群[1] 李建良[1] 景旭[1] 何煜 

机构地区：[1]西北农林科技大学信息工程学院,陕西杨凌712100 [2]陕西科技资源统筹中心共性技术推广部,陕西西安710077

出　　处：《计算机应用与软件》2015年第11期11-13,共3页Computer Applications and Software

基　　金：国家科技支撑计划项目(2013BAD15B02);中央高校基本科研业务费项目(QN2011036);科技支疆计划项目(2013AB 016)

摘　　要：由于标准遗传算法一般使用固定的交叉和变异概率,并且容易出现局部收敛、早熟等现象,降低了遗传算法的全局搜索效率。针对这种情况,提出一种改进遗传算法。在使用实数编码的前提下,对适应度函数进行优化改进,避免过早收敛,对变异和交叉算法进行优化,使得交叉和变异概率能够自适应调整,增加种族的多样性,确保算法的全局搜索能力。实验结果表明,改进之后的遗传算法可以达到很高的路径覆盖率,搜索效率比标准遗传算法高。Since standard genetic algorithms generally use fixed crossover probability and mutation probability, and are prone to appear local convergence, prematurity, etc. , this reduces the global searching efficiency of genetic algorithms. In view of this situation, this paper presents an improved genetic algorithm. It optimises and improves the fitness function under the premise of using real number coding, thus avoids the premature convergence and optimises the mutation and crossover algorithms, makes the mutation and crossover probabilities be able to adjust self-adaptively, and increases the diversity of population as well, ensures the global search ability of the algorithm. EXperimental results show that the improved genetic algorithm can achieve very high path coverage rate, its search efficiency is higher than the standard genetic algorithm.

关 键 词：遗传算法 适应度函数 测试用例 遗传算子 自适应 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]