计算机与现代化Issue(2)：36-39,44,5.DOI:10.3969/j.issn.1006-2475.2017.02.007
基于DA-SVM的软件缺陷预测模型
Software Defect Prediction Model Based on DA-SVM
甘露 1臧洌 1李航1
作者信息
- 1. 南京航空航天大学计算机科学与技术学院,江苏 南京 210016
- 折叠
摘要
Abstract
Feature extraction is an important step in software defect prediction technology research. However, the existing feature extraction cannot accurately obtain the nonlinear dependence relations among features, thus these methods are unable to improve the accuracy of software defect prediction model. In this paper, to solve this question we propose a software defect prediction mod-el ( Denoising Autoencoder Support Vector Machine, DA-SVM) which is based on denoising autoencoder and Support Vector Ma-chine. Firstly, the model extracts features by using denoising autoencoder, secondly uses these features as input of support vector machine, lastly, uses this model to predict bugs. Experimental results show that DA-SVM not only improves the accuracy of soft-ware defect prediction model, but also reduces the noise of history data and enhances the robustness of the software defect predic-tion model.关键词
特征提取/软件缺陷预测/降噪自动编码器/支持向量机Key words
feature extraction/software defect prediction/denoising autoencoder/support vector machine分类
信息技术与安全科学引用本文复制引用
甘露,臧洌,李航..基于DA-SVM的软件缺陷预测模型[J].计算机与现代化,2017,(2):36-39,44,5.
