摘要
针对传统的软件缺陷预测模型存在预测准确率低和适应性差的问题,本文提出了一种改进的PSO算法(IVPSO),并将其与BP神经网络相结合,以此来构建一个新的、预测性能和效果更加优秀的模型——IVPSO-BP.首先,对粒子群算法进行改进并利用其对BP网络进行优化;其次,基于优化的BP算法去建立一个预测模型;最后,将该模型与PSO-BP模型、J48(传统的机器学习方法)、BP进行实验比较.通过对最终实验的数据进行分析表明,IVPSO-BP模型具有更高的寻优性能和准确性.
In view of the traditional software defect prediction model,the problem of low accuracy and poor adaptability,this paper proposes a new software prediction model（IVPSO-BP）with better performance and effect,and combining with two by improving PSO algorithm for optimizing BP.Firstly,To improve Particle Swarm Optimization,and use it to optimize BP.Secondly,this paper employs optimized BP to build a prediction model.Finally,compares the experiment results with other machine learning methods-BP,J48 and PSO-BP.Through analyzing the data of the final experiment,the results indicated that proposed method owe a higher prediction precision.
出处
《微电子学与计算机》
CSCD
北大核心
2017年第4期39-43,48,共6页
Microelectronics & Computer
基金
国家自然科学基金(61300169)
关键词
软件缺陷预测
粒子群算法
神经网络
ssoftware defect detection
particle wwarm optimization（PSO）
artificial neural network（ANN）
