[1]赵越. 基于支持向量机的软件质量评价[J].计算机技术与发展,2015,25(12):76-79.
　ZHAO Yue. Evaluation of Software Quality Based on Support Vector Machine[J].,2015,25(12):76-79.
点击复制

 基于支持向量机的软件质量评价()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年12期
页码:
76-79
栏目:
智能、算法、系统工程
出版日期:
2015-12-10

文章信息/Info

Title:
 Evaluation of Software Quality Based on Support Vector Machine
文章编号:
1673-629X（2015)12-0076-04
作者:
 赵越
 渤海大学 信息科学与技术学院
Author(s):
 ZHAO Yue
关键词:
 ; 支持向量机; 数学模型; 软件质量评价; 指标体系
Keywords:
 ; support vector machine; mathematical model; software quality evaluation; index system
分类号:
TP31
文献标志码:
A
摘要:
 软件质量是计算机软件评价中的一个重要参考条件,在软件开发和维护中占有重要地位. 为了能够准确、科学地评价软件质量,文中基于支持向量机技术展开研究. 首先,构建数学模型,数学模型是进行质量评价的基础,文中在理解支持向量机理论的基础上重点描述了模型构建过程以及核函数的选择问题;然后,根据其他质量模型中的参考因素合理地构建了评价指标体系结构,指标体系包括7个一级指标和21个二级指标;最后,通过对样本数据进行实例分析和仿真训练判断软件质量评价方法的可行性. 拟合结果表明,基于支持向量机技术评价软件质量的方法客观合理,具有一定的实用性,是一种进行软件质量评价的有效方法.
Abstract:
 Software quality evaluation is an important reference condition in the evaluation of computer software and plays an important role in the software development and software maintenance. In order to accurately and scientifically evaluate software quality,based on support vector machine technology,the study is conducted. Firstly,construct a mathematical model,mathematical model is the basis for quality evaluation,based on understanding support vector machine theory focus on describing the model-building process as well as the choice of kernel function. Then,according to the reference factors in other quality model,reasonably construct evaluation index system structure,including seven first grade indexes and twenty-one second grade indexes. Finally,determine the feasibility of software quality e-valuation method through the example analysis and simulation training of sample data. Fitting results show that the evaluation method based on support vector machine technology for software quality is objective and reasonable,has practicability and is an effective method for conducting software quality evaluation.

相似文献/References:

[1]李雷 张建民.一种改善的基于支持向量机的边缘检测算子[J].计算机技术与发展,2010,(03):125.
　LI Lei,ZHANG Jian-min.An Improved Edge Detector Using the Support Vector Machines[J].,2010,(12):125.
[2]陈俏 曹根牛 陈柳.支持向量机应用于大气污染物浓度预测[J].计算机技术与发展,2010,(01):247.
　CHEN Qiao,CAO Gen-niu,CHEN Liu.Application of Support Vector Machine to Atmospheric Pollution Prediction[J].,2010,(12):247.
[3]李晶 姚明海.基于支持向量机的语义图像分类研究[J].计算机技术与发展,2010,(02):75.
　LI Jing,YAO Ming-hai.Research of Semantic Image Classification Based on Support Vector Machine[J].,2010,(12):75.
[4]姜鹤 陈丽亚.SVM文本分类中一种新的特征提取方法[J].计算机技术与发展,2010,(03):17.
　JIANG He,CHEN Li-ya.A New Feature Selection Method in SVM Text Categorization[J].,2010,(12):17.
[5]曹庆璞 董淑福 罗赟骞.网络时延的混沌特性分析及预测[J].计算机技术与发展,2010,(04):43.
　CAO Qing-pu,DONG Shu-fu,LUO Yun-qian.Chaotic Analysis and Prediction of Internet Time- Delay[J].,2010,(12):43.
[6]路川 胡欣杰.区域航空市场航线客流量预测研究[J].计算机技术与发展,2010,(04):84.
　LU Chuan,HU Xin-jie.Analysis of Regional Airline Passenger Forecast Title[J].,2010,(12):84.
[7]黄炜 黄志华.一种基于遗传算法和SVM的特征选择[J].计算机技术与发展,2010,(06):21.
　HUANG Wei,HUANG Zhi-hua.Feature Selection Based on Genetic Algorithm and SVM[J].,2010,(12):21.
[8]孙秋凤.microRNA计算识别中的模式识别技术[J].计算机技术与发展,2010,(06):97.
　SUN Qiu-feng.Pattern Recognition Technology for MicroRNA Identification[J].,2010,(12):97.
[9]刘振岩 王勇 陈立平 马俊杰 陈天恩.基于SVM的农业智能决策Web服务的研究与实现[J].计算机技术与发展,2010,(06):213.
　LIU Zhen-yan,WANG Yong,CHEN Li-ping,et al.Research and Implementation of Intelligence Decision Web Services Based on SVM for Digital Agriculture[J].,2010,(12):213.
[10]王李冬.一种新的人脸识别算法[J].计算机技术与发展,2009,(05):147.
　WANG Li-dong.A New Algorithm of Face Recognition[J].,2009,(12):147.
[11]吴家皋[][],余浩[] [],张雪英[]. 基于链接回溯的地理信息更新主题爬虫研究[J].计算机技术与发展,2014,24(07):52.
　WU Jia-gao[] [],YU Hao[] [],ZHANG Xue-ying[]. Study of Topic-driven Web Crawler for Geographic Information Updating Based on Link Backtracking[J].,2014,24(12):52.
[12]李雷,魏蕴婕. 结合模糊聚类与支持向量机的图像分割[J].计算机技术与发展,2014,24(07):88.
　LI Lei,WEI Yun-jie. Image Segmentation Combined FCM and SVM[J].,2014,24(12):88.
[13]李燕玲,苏一丹. 改进的二叉树支持向量机在多分类中的应用[J].计算机技术与发展,2014,24(07):181.
　LI Yan-ling,SU Yi-dan. Application of Improved Binary Tree Support Vector Machine in Multi-classification[J].,2014,24(12):181.
[14]李春生[],苏晓伟[],魏军[],等. 基于支持向量机的抽油机井功图识别研究[J].计算机技术与发展,2014,24(08):215.
　LI Chun-sheng[],SU Xiao-wei[],WEI Jun[],et al. Research on Diagrams Identification of Pumping Unit Based on Support Vector Machine[J].,2014,24(12):215.
[15]李妍坊,许歆艺,刘功申. 面向情感倾向性识别的特征分析研究[J].计算机技术与发展,2014,24(09):33.
　LI Yan-fang,XU Xin-yi,LIU Gong-shen. Research on Feature Analysis Oriented Text Sentiment Identification[J].,2014,24(12):33.
[16]鄂旭[] [],毕佳娜[],侯建[],等. 一种车牌智能定位方法研究[J].计算机技术与发展,2014,24(10):124.
　E Xu[] [],BI Jia-na[],HOU Jian[],et al. Research on a License Plate Intelligent Localization Method[J].,2014,24(12):124.
[17]李利杰[],张君华[],熊伟清[],等. 一种改进的支持向量机模型优化算法[J].计算机技术与发展,2014,24(12):114.
　LI Li-jie[],ZHANG Jun-hua[],XIONG Wei-qing[],et al. An Improved Algorithm for Model Optimization of Support Vector Machine[J].,2014,24(12):114.
[18]陈桂林,王生光,徐静妹,等. 基于GA和组合核的SVM入侵检测算法[J].计算机技术与发展,2015,25(02):148.
　CHEN Gui-lin,WANG Sheng-guang,XU Jing-mei,et al. Intrusion Detection Algorithm of SVM Based on GA and Composed Kernel Function[J].,2015,25(12):148.
[19]李琼,陈利. 一种改进的支持向量机文本分类方法[J].计算机技术与发展,2015,25(05):78.
　LI Qiong CHEN Li. An Improved Text Classification Method for Support Vector Machine[J].,2015,25(12):78.
[20]贾慧敏. 基于ML改进技术的IDS的设计与实现[J].计算机技术与发展,2015,25(06):114.
　JIA Hui-min. Design and Implementation of IDS Based on ML’ s Improved Technology[J].,2015,25(12):114.

更新日期/Last Update: 2016-01-28