Comprehensive Evaluation of Software Quality Based on LM-BP Neural Network

被引：0
|
作者：
Wang, Anbang [1 ]
Guo, Lihong [2 ]
Chen, Yuan [2 ]
Wang, Junjie [2 ]
Song, Yuanzhang [2 ]
机构：
[1] Univ Chinese Acad Sci, Changchun Inst Opt Fine Mech & Phys, Chinese Acad Sci, Changchun, Jilin, Peoples R China
[2] Chinese Acad Sci, Changchun Inst Opt Fine Mech & Phys, Changchun, Jilin, Peoples R China
来源：
2017 FOURTH INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND THEIR APPLICATIONS (DSA 2017) | 2017年
关键词：
software quality; comprehensive evaluation; artificial neural network; Levenberg Marquardt Back Propagation algorithm;
D O I：
10.1109/DSA.2017.37
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
In view of the shortcomings of traditional software quality evaluation methods, such as subjective and lack of self-learning ability, a comprehensive evaluation method of software quality based on Levenberg Marquardt Back Propagation (LM-BP) neural network is proposed. Comprehensive evaluation system of software quality is established based on ISO/IEC 9126 software quality model, and LM-BP algorithm is used to solve the problem of standard BP algorithm, such as slow convergence rate. A comprehensive evaluation model of software quality based on LM-BP neural network is established, which provides a new method and idea for comprehensive evaluation of software quality. The experimental results show that the comprehensive evaluation of software quality based on LM-BP neural network can obtain the comprehensive evaluation result of software quality quickly and accurately, and has good objectivity and practicability.
引用
收藏
页码：162 / 167
页数：6
相关论文
共 50 条
  • [1] Computer Network Security Evaluation Based on LM-BP Neural Network
    Huo, Zhenquan
    [J]. 2018 4TH INTERNATIONAL CONFERENCE ON ENVIRONMENTAL SCIENCE AND MATERIAL APPLICATION, 2019, 252
  • [2] A Radar Fault Prediction Based on LM-BP Neural Network
    Liu, S. H.
    Bi, Z. J.
    Zhang, W.
    [J]. INDUSTRIAL INSTRUMENTATION AND CONTROL SYSTEMS, PTS 1-4, 2013, 241-244 : 293 - 297
  • [3] Risk Assessment of Power Communication Network Based on LM-BP Neural Network
    Wang, Yanan
    Wang, Ke
    Zhang, Ran
    Xue, Qiao
    Chen, Xiangzhou
    Zhang, Geng
    [J]. 2018 INTERNATIONAL SYMPOSIUM ON POWER ELECTRONICS AND CONTROL ENGINEERING (ISPECE 2018), 2019, 1187
  • [4] Estimation of forest volume based on LM-BP neural network model
    [J]. Wu, D. (dashengwu@sina.cn), 1600, Transport and Telecommunication Institute, Lomonosova street 1, Riga, LV-1019, Latvia (18):
  • [5] Angular Acceleration Sensor Fault Diagnosis Based on LM-BP Neural Network
    Liu, Hua
    Li, Bo
    Liu, Tong
    Wang, Meiling
    Fu, Huijin
    Guo, Ruoyu
    [J]. 2018 37TH CHINESE CONTROL CONFERENCE (CCC), 2018, : 6028 - 6032
  • [6] Research on Prediction Model of Stock Price Based on LM-BP Neural Network
    Li Feng
    [J]. PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON LOGISTICS, ENGINEERING, MANAGEMENT AND COMPUTER SCIENCE, 2014, 101 : 774 - 776
  • [7] Electromyography based Handwriting Recognition System using LM-BP Neural Network
    Tan, Shiying
    An, Yueying
    Wu, Yulun
    Zhang, Dingguo
    [J]. 2016 9TH INTERNATIONAL CONFERENCE ON HUMAN SYSTEM INTERACTIONS (HSI), 2016, : 83 - 88
  • [8] Keyword Extraction for Web News Documents Based on LM-BP Neural Network
    Liu, Xiaohui
    Yan, Xin
    Yu, Zhengtao
    Qin, Guangshun
    Mo, Yuanyuan
    [J]. 2015 27TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC), 2015, : 2525 - 2531
  • [9] LOGGING EVALUATION OF SHALE GAS-BEARING PROPERTIES BASED ON LM-BP NEURAL NETWORK MODEL
    Zheng, Wei
    Liu, Yunfeng
    [J]. FRESENIUS ENVIRONMENTAL BULLETIN, 2020, 29 (9A): : 8347 - 8354
  • [10] Predictive Model of BOF Based on LM-BP Neural Network Combining with Learning Rate
    Ding, Xiying
    Wang, Jian
    Yang, Shuping
    [J]. 2009 SECOND INTERNATIONAL SYMPOSIUM ON KNOWLEDGE ACQUISITION AND MODELING: KAM 2009, VOL 2, 2009, : 155 - 157
12345