• DocumentCode
    324641
  • Title

    Software quality measurement: concepts and fuzzy neural relational model

  • Author

    Pedrycz, W. ; Peters, J.F. ; Ramanna, S.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Manitoba Univ., Winnipeg, Man., Canada
  • Volume
    2
  • fYear
    1998
  • fDate
    4-9 May 1998
  • Firstpage
    1026
  • Abstract
    A fuzzy neural relational model of software quality derived from the McCall hierarchical software quality measurement framework (HSQF), is introduced. The HSQF has three fundamental levels (factors→criteria→metrics) which has a rather natural generalization in the context of fuzzy sets. Vectors of factors, criteria, and metrics are treated as fuzzy sets. On each level, fuzzy objects (fuzzy set and fuzzy relation) are introduced. A learning algorithm is proposed to calibrate the relations at the topmost levels of the software quality model. A learning scenario and detailed learning formulas are given. A brief illustration of the model is also given
  • Keywords
    fuzzy set theory; learning (artificial intelligence); neural nets; software metrics; software quality; McCall hierarchical software quality measurement framework; fuzzy neural relational model; fuzzy objects; fuzzy sets; learning algorithm; Computational intelligence; Electric variables measurement; Fuzzy sets; Laboratories; Q factor; Quality management; Software measurement; Software metrics; Software quality; Software testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on
  • Conference_Location
    Anchorage, AK
  • ISSN
    1098-7584
  • Print_ISBN
    0-7803-4863-X
  • Type

    conf

  • DOI
    10.1109/FUZZY.1998.686259
  • Filename
    686259