• DocumentCode
    1977628
  • Title

    Software Architecture Decomposition Using Clustering Techniques

  • Author

    Alkhalid, Abdulaziz ; Chung-Horng Lung ; Duo Liu ; Ajila, Samuel

  • Author_Institution
    Dept. of Syst. & Comput. Eng., Carleton Univ., Ottawa, ON, Canada
  • fYear
    2013
  • fDate
    22-26 July 2013
  • Firstpage
    806
  • Lastpage
    811
  • Abstract
    While applying clustering techniques to software system decomposition, the software designer faces two practical issues: (1) determination of the number of clusters that will be mapped to software modules and (2) determination of a specific cluster or software module for some highly coupled components. This paper presents an approach for software architecture decomposition with an emphasis on finding solutions to those two issues. The approach uses fuzzy c-means clustering together with three hierarchical agglomerative clustering methods and the adaptive K-nearest neighbor algorithm. We applied the approach to real industrial software systems. The results show that our approach provides objective and insightful information to the software designer in dealing with those two issues.
  • Keywords
    fuzzy set theory; pattern clustering; software architecture; adaptive K-nearest neighbor algorithm; fuzzy c-means clustering; hierarchical agglomerative clustering methods; real industrial software systems; software architecture decomposition; software designer; software modules; software system decomposition; Clustering algorithms; Computer architecture; Protocols; Software algorithms; Software architecture; Software systems; A-KNN; CLINK; SLINK; Software architecture; WPGMA; clustering; fuzzy c-means; reengineering; software decomposition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Software and Applications Conference (COMPSAC), 2013 IEEE 37th Annual
  • Conference_Location
    Kyoto
  • Type

    conf

  • DOI
    10.1109/COMPSAC.2013.132
  • Filename
    6649921