Using the genetic algorithm to build optimal neural networks for fault-prone module detection

被引:12
|
作者
Hochman, R [1 ]
Khoshgoftaar, TM [1 ]
Allen, EB [1 ]
Hudepohl, JP [1 ]
机构
[1] FLORIDA ATLANTIC UNIV,DEPT COMP SCI & ENGN,BOCA RATON,FL 33431
来源
SEVENTH INTERNATIONAL SYMPOSIUM ON SOFTWARE RELIABILITY ENGINEERING, PROCEEDINGS | 1996年
关键词
backpropagation; classification problem; fault-prone module; fitness function; genetic algorithm; neural network; simulated evolution; software engineering problem; software metrics; software quality;
D O I
10.1109/ISSRE.1996.558759
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
引用
收藏
页码:152 / 162
页数:11
相关论文
共 50 条
  • [41] Iterative Identification of Fault-Prone Binaries Using In-Process Metrics
    Layman, Lucas
    Kudrjavets, Gunnar
    Nagappan, Nachiappan
    ESEM'08: PROCEEDINGS OF THE 2008 ACM-IEEE INTERNATIONAL SYMPOSIUM ON EMPIRICAL SOFTWARE ENGINEERING AND MEASUREMENT, 2008, : 206 - +
  • [42] Detection of Fault-Prone Classes Using Logistic Regression Based Object-Oriented Metrics Thresholds
    Hussain, Shahid
    Keung, Jacky
    Khan, Arif Ali
    Bennin, Kwabena Ebo
    2016 IEEE INTERNATIONAL CONFERENCE ON SOFTWARE QUALITY, RELIABILITY AND SECURITY COMPANION (QRS-C 2016), 2016, : 93 - 100
  • [43] A metric to detect fault-prone software modules using text filtering
    Mizuno, O. (o-mizuno@kit.ac.jp), 1600, Inderscience Enterprises Ltd., 29, route de Pre-Bois, Case Postale 856, CH-1215 Geneva 15, CH-1215, Switzerland (07):
  • [44] An electrical motor fault detection scheme based on improved genetic algorithm and optimal neural network
    Ren, Bin
    Liu, Huijie
    Yang, Lei
    Cheng, Lianglun
    Journal of Theoretical and Applied Information Technology, 2012, 45 (01) : 273 - 277
  • [45] Global Synchronization and Consensus Using Beeps in a Fault-Prone Multiple Access Channel
    Hounkanli, Kokouvi
    Miller, Avery
    Pelc, Andrzej
    THEORETICAL COMPUTER SCIENCE, 2020, 806 (806) : 567 - 576
  • [46] Artificial neural network-based metric selection for software fault-prone prediction model
    Jin, C.
    Jin, S. -W.
    Ye, J. -M.
    IET SOFTWARE, 2012, 6 (06) : 479 - 487
  • [47] Bearing Fault Detection Using Neural Networks
    Hajar, Mayssa
    Khalil, Mohamad
    2012 2ND INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTATIONAL TOOLS FOR ENGINEERING APPLICATIONS (ACTEA), 2012, : 57 - 60
  • [48] Optimal feature selection using genetic algorithm for mechanical fault detection of induction motor
    Nguyen, Ngoc-Tu
    Lee, Hong-Hee
    Kwon, Jeong-Min
    JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, 2008, 22 (03) : 490 - 496
  • [49] Optimal feature selection using genetic algorithm for mechanical fault detection of induction motor
    Ngoc-Tu Nguyen
    Hong-Hee Lee
    Jeong-Min Kwon
    Journal of Mechanical Science and Technology, 2008, 22 : 490 - 496
  • [50] An Integrated Approach to Detect Fault-Prone Modules Using Complexity and Text Feature Metrics
    Mizuno, Osamu
    Hata, Hideaki
    ADVANCES IN COMPUTER SCIENCE AND INFORMATION TECHNOLOGY, PROCEEDINGS, 2010, 6059 : 457 - +