约减的自适应粒子群算法的测试数据自动生成  

Test Data Automatic Generation Based on RAPSO

作　　者：焦重阳 周清雷[1] 张文宁[2,3] 

机构地区：[1]郑州大学信息工程学院,郑州450001 [2]中国人民解放军信息工程大学 [3]中原工学院,郑州450001

出　　处：《小型微型计算机系统》2017年第6期1294-1298,共5页Journal of Chinese Computer Systems

基　　金：国家自然科学基金项目(61250007)资助

摘　　要：针对基本粒子群算法易陷入局部极值呈现早熟性、收敛速度慢、精度低等问题,提出一种约减的自适应粒子群优化算法(RAPSO)的软件结构测试数据自动生成方法.对基本粒子群进化方程进行约减,提出基于惯性权重的自适应调整方案,将惯性权重直接作用于粒子的位置,以分支函数叠加法作为适应值函数.RAPSO去掉了PSO进化方程的粒子速度项而使原来的二阶微分方程简化为一阶微分方程,仅由粒子位置控制进化过程.针对三角形判定程序的结构测试数据自动生成进行实验.实验结果表明,该方法可以更高效地自动生成测试数据.Recently, meta-heuristic search optimization algorithms has been confirmed to be an effective way to generate software structural test data automatically. According to the basic particle swarm optimization algorithm that easily falls into the local optimal solution and the problem of low convergence rate and low search accuracy, this paper proposes reduced adaptive particle swarm optimi- zation algorithm for generating software structural test data automatically. This paper reduces the evolution equations of particle swarm, adjustment scheme based on inertia weight is proposed, inertia weight is directly acted on the particle position,and employs the summation of branch-function as fitness function. The RAPSO discards the particle velocity and reduces the PSO from the second order to the first order difference equation. The evolutionary process is only controlled by the variables of the particles position. Triangle discrimination programs are used to experiment. The experimental results show that our approach can effectively improve the efficiency of generating test data automatically.

关 键 词：粒子群算法 测试数据自动生成 进化方程约减 自适应 惯性权重 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]