• DocumentCode
    3407551
  • Title

    Traceability ReARMed

  • Author

    David, Joern ; Koegel, Maximilian ; Naughton, Helmut ; Helming, Jonas

  • Author_Institution
    Inst. of Comput. Sci. II, Tech. Univ. Munich, Garching, Germany
  • Volume
    1
  • fYear
    2009
  • fDate
    20-24 July 2009
  • Firstpage
    340
  • Lastpage
    348
  • Abstract
    Traceability links connect artifacts in software engineering models to allow tracing in a variety of use cases. Common to any of these use cases is that one can easily find related artifacts by following these links. Traceability links can significantly reduce the risk and cost of change in a software development project. However, finding, creating and maintaining these links is costly in most cases. In any real-world project of significant size the creation and maintenance of traceability links requires tool support. In this paper, we propose a novel approach to support the automation of traceability link recovery based on association rule mining and operation-based change tracking. Traceability link recovery is the activity of finding missing or lost traceability links. Our approach automatically generates a list of candidate links based on the project history along with a score of support and confidence for every candidate link. We transformed the data from an operation based change tracking system to sets of frequent items, which serve as input for association rule mining (ARM). We applied our approach to data from a software development project with more than 40 developers and assessed the quality of the candidate links in interviews.
  • Keywords
    data mining; project management; software development management; software quality; ReARMed; association rule mining; operation-based change tracking; project history; software development project; software engineering model; software quality; tool support; traceability link recovery; use case; Association rules; Content based retrieval; Data mining; Guidelines; History; Indexing; Information retrieval; Large scale integration; Programming; Software engineering; Association Rule Mining; Traceability; Unified Model;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Software and Applications Conference, 2009. COMPSAC '09. 33rd Annual IEEE International
  • Conference_Location
    Seattle, WA
  • ISSN
    0730-3157
  • Print_ISBN
    978-0-7695-3726-9
  • Type

    conf

  • DOI
    10.1109/COMPSAC.2009.52
  • Filename
    5254243