基于RACPSO的测试用例自动生成方法  

Automatic Generation Method of Test Case Based on RACPSO

作　　者：贺滢[1] 徐蔚鸿[1] 李杨林[1] 

机构地区：[1]长沙理工大学计算机与通信工程学院,长沙410114

出　　处：《计算机工程》2016年第5期66-70,79,共6页Computer Engineering

基　　金：湖南省科技计划基金资助项目(2014SK4080)

摘　　要：针对粒子群优化算法在软件测试用例自动生成过程中存在后期收敛速度慢、易陷入局部最优及求解精度低的问题,基于约简的自适应混沌粒子群优化(RACPSO)算法,提出一种测试用例自动生成方法。将粒子群标准进化方程化简为无速度项的进化方程,设计基于适应度值自适应调整的惯性权重,实现粒子的位置更新。采用基于群体适应度方差的早熟收敛判断策略进行混沌搜索,并通过增加粒子的多样性克服早熟收敛现象。实验结果表明,与标准粒子群优化算法和自适应粒子群优化算法相比,RACPSO算法在收敛速度与求解效率方面更具优势。To solve the problem of slow convergence rate,high possibilities of being trapped in local optimum, and low solution accuracy in Particle Swarm Optimization（PSO） algorithm when applied to automatic software Test Case（TC） generation,this paper presents an automatic generation method of TC based on Reduced Adaptive Chaos Particle Swarm Optimization（RACPSO） algorithm. The original standard evolution equations of PSO are simplified as non-velocity evolution equation and adaptive inertia weight based on fitness value is proposed to update the position of the particles directly. Meanwhile, a particle premature convergence judgment strategy based on the fitness variance of particle swarm is used to judge the degree of PSO algorithm convergence. RACPSO increases the diversity of particles by applying chaos searching mechanism to guide the particle swarm to jump out of premature convergence as quickly as possible. Experimental results show that RACPSO has faster convergence rate and higher solving efficiency compared with Standard Particle Swarm Optimization（SPSO） algorithm and Adaptive Particle Swarm Optimization（APSO） algorithm.

关 键 词：粒子群算法 测试用例自动生成 进化方程约简 自适应惯性权重 混沌搜索 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]