| 基于卷积神经网络的构件分类策略的研究 |
| |
| 引用本文： | 张富为,杨秋翔,宋超峰.基于卷积神经网络的构件分类策略的研究[J].计算机工程与应用,2019,55(8):201-207. |
| |
| 作者姓名： | 张富为 杨秋翔 宋超峰 |
| |
| 作者单位： | 中北大学 软件学院,太原,030051;中北大学 软件学院,太原,030051;中北大学 软件学院,太原,030051 |
| |
| 摘 要： | 为了提高软件复用过程中构件检索的效率,分析了软件构件分类技术的优缺点以及构件特征,从构件刻面信息的角度,采用卷积神经网络技术,提出一种基于卷积神经网络的构件分类策略;利用卷积神经网络对构件刻面特征进行提取,减少人为因素,提高刻面信息提取精确性,并训练出基于卷积神经网络的构件分类模型,通过具体的实验,来论证该模型的准确性,以达到提高构件检索效率的目的。
|
| 关 键 词： | 软件复用 软件构件 卷积神经网络 构件分类 构件检索 |
Research on Component Classification Strategy Based on Convolution Neural Network |
| |
| ZHANG Fuwei,YANG Qiuxiang,SONG Chaofeng.Research on Component Classification Strategy Based on Convolution Neural Network[J].Computer Engineering and Applications,2019,55(8):201-207. |
| |
| Authors: | ZHANG Fuwei YANG Qiuxiang SONG Chaofeng |
| |
| Affiliation: | College of Software, North University of China, Taiyuan 030051, China |
| |
| Abstract: | To improve the efficiency of component retrieval in software reuse process, advantages and disadvantages of software component classification technology and component features are analyzed. From the aspect of component faceting information, convolution neural network technology is used to propose a component based on convolution neural network. The convolutional neural network is used to extract the facet features, reduce the human factors, improve the accuracy of faceted information extraction, and train the component classification model based on convolution neural network. Through the specific experiments, the model is demonstrated to improve the efficiency of component retrieval. |
| |
| Keywords: | software reuse software component convolution neural network component classification component retrieval |
| 本文献已被 维普 万方数据 等数据库收录！ |
| 点击此处可从《计算机工程与应用》浏览原始摘要信息 |
|
点击此处可从《计算机工程与应用》下载全文 |
|
