• DocumentCode
    657501
  • Title

    Automated classification of NASA anomalies using natural language processing techniques

  • Author

    Falessi, Davide ; Layman, Lucas

  • Author_Institution
    Fraunhofer CESE, College Park, MD, USA
  • fYear
    2013
  • fDate
    4-7 Nov. 2013
  • Firstpage
    5
  • Lastpage
    6
  • Abstract
    NASA anomaly databases are rich resources of software failure data in the field. These data are often captured in natural language that is not appropriate for trending or statistical analyses. This fast abstract describes a feasibility study of applying 60 natural language processing techniques for automatically classifying anomaly data to enable trend analyses.
  • Keywords
    aerospace computing; natural language processing; pattern classification; NASA anomaly databases; National Aeronautics and Space Administration; anomaly data classification; natural language processing techniques; software failure data; statistical analysis; trending analysis; Databases; Educational institutions; Market research; NASA; Natural language processing; Software; NLP; natural language processing; software failure;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Software Reliability Engineering Workshops (ISSREW), 2013 IEEE International Symposium on
  • Conference_Location
    Pasadena, CA
  • Type

    conf

  • DOI
    10.1109/ISSREW.2013.6688849
  • Filename
    6688849
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=657501