• DocumentCode
    695462
  • Title

    Toward a learned project-specific fault taxonomy: application of software analytics

  • Author

    Kidwell, Billy ; Hayes, Jane

  • Author_Institution
    Comput. Sci. Dept., Univ. of Kentucky Lexington, Lexington, KY, USA
  • fYear
    2015
  • fDate
    2-2 March 2015
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    This position paper argues that fault classification provides vital information for software analytics, and that machine learning techniques such as clustering can be applied to learn a project- (or organization-) specific fault taxonomy. Anecdotal evidence of this position is presented as well as possible areas of research for moving toward the posited goal.
  • Keywords
    fault diagnosis; learning (artificial intelligence); pattern classification; pattern clustering; program diagnostics; clustering; fault classification; learned project-specific fault taxonomy; machine learning technique; organization-specific fault taxonomy; software analytics; Automation; Inspection; Organizations; Software; Software engineering; Taxonomy; Testing; fault taxonomy; machine learning; softwarerepositories; clustering;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Software Analytics (SWAN), 2015 IEEE 1st International Workshop on
  • Conference_Location
    Montreal, QC
  • Type

    conf

  • DOI
    10.1109/SWAN.2015.7070479
  • Filename
    7070479
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=695462