Software Defect Prediction Model Based on IVPSO-BP

DENG Wei-kang; LIU Feng; ZHU Er-zhou

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Software Defect Prediction Model Based on IVPSO-BP

更新时间：2026-09-04

- Software Defect Prediction Model Based on IVPSO-BP
- Vol. 34, Issue 4, Pages: 39-43(2017)
- 作者机构：
  
  安徽大学计算机科学与技术学院,安徽,合肥,230601
- 作者简介：
- 基金信息：
- DOI：
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- Published：2017
- 稿件说明：
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邓伟康, 刘锋, 朱二周. 基于新型PSO算法优化BP神经网络的软件缺陷预测方法研究[J]. 微电子学与计算机, 2017, 34(4): 39-43, 48.

DENG Wei-kang, LIU Feng, ZHU Er-zhou. Software Defect Prediction Model Based on IVPSO-BP[J]. 2017, 34(4): 39-43.
邓伟康, 刘锋, 朱二周. 基于新型PSO算法优化BP神经网络的软件缺陷预测方法研究[J]. 微电子学与计算机, 2017, 34(4): 39-43, 48. DOI：

DENG Wei-kang, LIU Feng, ZHU Er-zhou. Software Defect Prediction Model Based on IVPSO-BP[J]. 2017, 34(4): 39-43. DOI：

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.

Abstract

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.

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references

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姜慧研, 宗茂, 刘相莹. 基于ACO-SVM的软件缺陷预测模型的研究[J]. 计算机学报, 2011, 34(6): 1148-1154. DOI: 1148

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