• DocumentCode
    1942652
  • Title

    Comparison of Artificial Neural Network and Regression Models in Software Effort Estimation

  • Author

    De Barcelos, Iris Fabiana Tronto ; Silva, José Demísio Simões da ; Sant´Anna, Nilson

  • Author_Institution
    Brazilian Nat. Inst. for Space Res., Campos
  • fYear
    2007
  • fDate
    12-17 Aug. 2007
  • Firstpage
    771
  • Lastpage
    776
  • Abstract
    Good practices in software project management are basic requirements for companies to stay in the market, because the effective project management leads to improvements in product quality and cost reduction. Fundamental measurements are the prediction of size, effort, resources, cost and time spent in the software development process. In this paper, predictive Artificial Neural Network (ANN) and Regression based models are investigated, aiming at establishing simple estimation methods alternatives. The results presented in this paper compare the performance of both methods and show that artificial neural networks are effective in effort estimation.
  • Keywords
    neural nets; project management; regression analysis; software management; artificial neural network; cost reduction; product quality; regression based models; regression models; software development process; software effort estimation; software project management; Accuracy; Artificial neural networks; Costs; Iris; Mathematical model; Predictive models; Programming; Project management; Size measurement; Software quality;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2007. IJCNN 2007. International Joint Conference on
  • Conference_Location
    Orlando, FL
  • ISSN
    1098-7576
  • Print_ISBN
    978-1-4244-1379-9
  • Electronic_ISBN
    1098-7576
  • Type

    conf

  • DOI
    10.1109/IJCNN.2007.4371055
  • Filename
    4371055