• DocumentCode
    2615401
  • Title

    Software reliability model selection

  • Author

    Knafl, George J. ; Sacks, Jerome

  • Author_Institution
    Dept. of Comput. Sci. & Inf. Syst., DePaul Univ., Chicago, IL, USA
  • fYear
    1991
  • fDate
    11-13 Sep 1991
  • Firstpage
    597
  • Lastpage
    601
  • Abstract
    Model selection based on a predictive performance measure is compared to model selection based on maximum likelihood. Both procedures exhibit unstable relative performance when predictive performance is measured over time periods that may contain overly small proportions of total failures. Both procedures may be stabilized by bounding this proportion away from zero. In that case, both procedures exhibit similar predictive performance relative to the other. This provides evidence that model selection based on the commonly used maximum likelihood approach may in fact have good predictive performance
  • Keywords
    software reliability; maximum likelihood; model selection; predictive performance measure; software reliability; total failures; unstable relative performance; Computer science; Information systems; Maximum likelihood estimation; Parameter estimation; Predictive models; Software engineering; Software measurement; Software reliability; Statistics; Time measurement;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Software and Applications Conference, 1991. COMPSAC '91., Proceedings of the Fifteenth Annual International
  • Conference_Location
    Tokyo
  • Print_ISBN
    0-8186-2152-4
  • Type

    conf

  • DOI
    10.1109/CMPSAC.1991.170245
  • Filename
    170245