摘要
测试性指标的优化分配关系到改进系统测试性水平所需的费用。建立了基于费用函数的测试性指标优化分配模型。根据费用函数需满足的基本要求,以费用最小为目标,设计了一种实用的非线性费用函数。分析了费用函数中各参数对费用的影响以及各参数的获取方法。针对该非线性规划问题,在给定的系统测试性指标下,采用遗传算法求解全局最优解。应用结果表明遗传算法能够获得合理的测试性分配值,费用函数可以用于系统测试性指标的优化分配。
Optimal allocation of testability index relates to the cost of improving the system testability level. A testability index optimal allocation model based on cost function is established. According to the basic requirements of the cost function, a practical nonlinear cost function is designed with minimum cost as the objective. The influence of each parameter in the cost function on cost and the acquisition methods of each parameter are analyzed. For the nonlinear programming problem, genetic algorithm is used to solve the global optimal solution under given system testability index. The application results show that the genetic algorithm can obtain reasonable testability allocation values, and the cost function can be used for the optimal allocation of testability index.
出处
《信息技术与网络安全》
2018年第2期51-54,62,共5页
Information Technology and Network Security
关键词
测试性
优化分配
费用函数
遗传算法
testability
optimal allocation
cost function
genetic algorithm