计算机应用与软件2018，Vol.35Issue(2)：36-43,8.DOI:10.3969/j.issn.1000-386x.2018.02.006
基于自学习的软件质量实时预警模型
SOFTWARE QUALITY REAL-TIME EARLY WARNING MODEL BASED ON SELF-LEARNING
刘原序 1韩培胜1
作者信息
- 1. 解放军信息工程大学密码工程学院 河南郑州450000
- 折叠
摘要
Abstract
The management and control of software quality are very important in the research of software development project management.However, the existing software quality analysis models are usually evaluated for software end products and are insensitive to changes in data sets and difficult to adapt to ever -changing development environment.By using the method of principal component analysis(PCA)and a self-generating incremental learning support vector machine(IL-SVM), we proposed a self-warning model of real-time software quality analysis model.The principal component analysis(PCA)was used to reduce the attribute dimension of the original dataset, and then the software quality warning was carried out by using the incremental learning SVM.Finally, the relevant experiments were carried out by using the NASA dataset.Combined with the actual situation of the analysis, the method had good results in quality warning.关键词
软件质量管理/支持向量机/属性约简/质量预警Key words
Software quality management/Support vector machine/Attribute reduction/Quality warning分类
信息技术与安全科学引用本文复制引用
刘原序,韩培胜..基于自学习的软件质量实时预警模型[J].计算机应用与软件,2018,35(2):36-43,8.
