• DocumentCode
    2746151
  • Title

    The effects of data mining techniques on software cost estimation

  • Author

    Lum, Karen T. ; Baker, Daniel R. ; Hihn, Jairus M.

  • Author_Institution
    Jet Propulsion Lab., California Inst. of Technol., Pasadena, CA
  • fYear
    2008
  • fDate
    28-30 June 2008
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    Current research at JPL incorporates data mining and machine learning techniques to see whether a better software cost model can be developed. 2CEE is a tool developed for developing new software cost estimation models using data mining techniques. The accuracy of these models has been validated internally through leave-one out cross validation. However, the newly generated models have not been validated to see how well they predict in the real world. Our study seeks to find out how well these machine learning based models perform against standard models for eighteen new flight and ground software projects. The accurate performance of the models against current real world projects is extremely important for practitioners to adapt new techniques.
  • Keywords
    data mining; learning (artificial intelligence); software cost estimation; data mining techniques; machine learning techniques; software cost estimation; software cost model; Calibration; Costs; Data mining; Laboratories; Machine learning; Nearest neighbor searches; Predictive models; Propulsion; Software performance; Software tools; Cost estimation; data mining; model performance; modeling software costs;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering Management Conference, 2008. IEMC Europe 2008. IEEE International
  • Conference_Location
    Estoril
  • Print_ISBN
    978-1-4244-2288-3
  • Electronic_ISBN
    978-1-4244-2289-0
  • Type

    conf

  • DOI
    10.1109/IEMCE.2008.4617949
  • Filename
    4617949