• DocumentCode
    3228762
  • Title

    Knowledge-Guided Methodology for Specification Analysis

  • Author

    Singh, Bawa ; Shankar, Ashwin ; Shiyanovskii, Y. ; Wolff, Francis ; Papachristou, C. ; Weyer, Daniel ; Clay, Steve ; Morrison, Jim

  • fYear
    2013
  • fDate
    4-6 Nov. 2013
  • Firstpage
    749
  • Lastpage
    754
  • Abstract
    The number of Soft-IP vendors and designsbecoming available on the global market is growing at a phenomenal rate. The current practice of evaluating Soft IPs using their specification is a time consuming manual process. A specification document is primarily written in English, which serves as a common language for internal product development teams as well as customers. Designers have a preference for writing specifications in an informal natural language using text and notations, including diagrams, charts and tables. The lack of formality of specification documents is a limiting factor in their analysis. The current state-of-the-art in hardware design lacks any specification analysis technique. In this paper, we present a knowledge-guided methodology for specification analysis that can automatically analyze specification documents. Our approach avoids formal specification. Instead we rely on domain-based ontologies to capture design behavior. We tested our approach by analyzing floating point specification from several third party IP vendors. We define spec coverage and requirement coverage metrics to quantify our results.
  • Keywords
    formal specification; knowledge based systems; ontologies (artificial intelligence); Soft IP; domain-based ontology; floating point specification; informal natural language; knowledge-guided methodology; requirement coverage metrics; spec coverage; specification analysis; specification document; Dictionaries; Feature extraction; Hardware; IP networks; Ontologies; Standards; XML; Expert systems; Knowledge base; Ontology; Soft-IP; Specification analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Tools with Artificial Intelligence (ICTAI), 2013 IEEE 25th International Conference on
  • Conference_Location
    Herndon, VA
  • ISSN
    1082-3409
  • Print_ISBN
    978-1-4799-2971-9
  • Type

    conf

  • DOI
    10.1109/ICTAI.2013.115
  • Filename
    6735326