• DocumentCode
    3259781
  • Title

    Extracting procedural knowledge from software systems using inductive learning in the PM system

  • Author

    Reynolds, Robert G. ; Maletic, Jonathan I.

  • Author_Institution
    Dept. of Comput. Sci., Wayne State Univ., Detroit, MI
  • fYear
    1992
  • fDate
    15-20 Jun 1992
  • Firstpage
    131
  • Lastpage
    139
  • Abstract
    The issue of software reuse has been found to be a much harder task than previously thought. Some of the problems are due to the lack of emphasis placed on non-functional requirements during the software development phase, such as maintainability and understandability. Other problems arise from the difficulty of defining precise criteria for considering a software module reusable. They are usually elusive, and vary dramatically from one domain to another. This paper presents PM, a software system the goal of which is the automation of the software reuse process. PM uses an incremental approach in performing analysis and storage of software modules, at different levels of granularity. Its fundamental characteristics are domain independence and flexibility, accomplished applying inductive learning techniques and analyzing reusable and nonreusable code examples
  • Keywords
    knowledge acquisition; knowledge based systems; learning (artificial intelligence); software reusability; PM system; Partial Metrics system; granularity; inductive learning; procedural knowledge acquisition; software development; software maintenance; software reuse; Automation; Performance analysis; Programming; Software performance; Software reusability; Software systems;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Software Engineering and Knowledge Engineering, 1992. Proceedings., Fourth International Conference on
  • Conference_Location
    Capri
  • Print_ISBN
    0-8186-2830-8
  • Type

    conf

  • DOI
    10.1109/SEKE.1992.227937
  • Filename
    227937