• DocumentCode
    555372
  • Title

    A combination approach for enhancing automated traceability: (NIER track)

  • Author

    Chen, Xiaofan ; Hosking, John ; Grundy, John

  • Author_Institution
    Dept. of Comput. Sci., Univ. of Auckland, Auckland, New Zealand
  • fYear
    2011
  • fDate
    21-28 May 2011
  • Firstpage
    912
  • Lastpage
    915
  • Abstract
    Tracking a variety of traceability links between artifacts assists software developers in comprehension, efficient development, and effective management of a system. Traceability systems to date based on various Information Retrieval (IR) techniques have been faced with a major open research challenge: how to extract these links with both high precision and high recall. In this paper we describe an experimental approach that combines Regular Expression, Key Phrases, and Clustering with IR techniques to enhance the performance of IR for traceability link recovery between documents and source code. Our preliminary experimental results show that our combination technique improves the performance of IR, increases the precision of retrieved links, and recovers more true links than IR alone.
  • Keywords
    information retrieval; program diagnostics; software engineering; automated traceability system; document clustering; information retrieval; key phrases; regular expression; traceability link recovery; Clustering algorithms; Documentation; Information retrieval; Large scale integration; Software; Thesauri; Unified modeling language; clustering; key phrases; regular expression; traceability;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Software Engineering (ICSE), 2011 33rd International Conference on
  • Conference_Location
    Honolulu, HI
  • ISSN
    0270-5257
  • Print_ISBN
    978-1-4503-0445-0
  • Electronic_ISBN
    0270-5257
  • Type

    conf

  • DOI
    10.1145/1985793.1985943
  • Filename
    6032550