English  |  正體中文  |  简体中文  |  Items with full text/Total items : 54367/62174 (87%)
Visitors : 14649369      Online Users : 81
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTHU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    National Tsing Hua University Institutional Repository > 電機資訊學院 > 資訊工程學系 > 期刊論文 >  A Study on the Applicability of Modified Genetic Algorithms for the Parameter Estimation of Software Reliability Modeling

    Please use this identifier to cite or link to this item: http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/82441

    Title: A Study on the Applicability of Modified Genetic Algorithms for the Parameter Estimation of Software Reliability Modeling
    Authors: C. J. Hsu;C. Y. Huang
    教師: 黃慶育
    Date: 2010
    Publisher: Institute of Electrical and Electronics Engineers
    Relation: Proceedings of the 34th Annual IEEE International Computer Software and Applications Conference (COMPSAC 2010), Seoul, South Korea, 19-23 July 2010, pp. 531-540
    Keywords: Genetic Algorithm
    Parameter Estimation
    Software Quality Assurance
    Software Reliability Growth Models
    Abstract: In order to assure software quality and assess software reliability, many software reliability growth models (SRGMs) have been proposed for estimation of reliability growth of products in the past three decades. In principle, two widely used methods for the parameter estimation of SRGMs are the maximum likelihood estimation (MLE) and the least squares estimation (LSE). However, the approach of these two estimations may impose some restrictions on SRGMs, such as the existence of derivatives from formulated models or the needs for complex calculation. Thus in this paper, we propose a modified genetic algorithm (MGA) to estimate the parameters of SRGMs. Experiments based on real software failure data are performed, and the results show that the proposed genetic algorithm is more effective and faster than traditional genetic algorithms.
    Relation Link: http://www.ieee.org/
    URI: http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/82441
    Appears in Collections:[資訊工程學系] 期刊論文
    [資訊系統與應用研究所] 會議論文

    Files in This Item:

    File Description SizeFormat


    SFX Query


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback