• DocumentCode
    2969201
  • Title

    Comparing some neural network models for software development effort prediction

  • Author

    Ghose, Mrinal Kanti ; Bhatnagar, Roheet ; Bhattacharjee, Vandana

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Sikkim Manipal Inst. of Technol., Rangpo, India
  • fYear
    2011
  • fDate
    4-5 March 2011
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Software development effort estimation is one of the most major activities in software project management. A number of models have been proposed to make software effort estimations but still no single model can predict the effort accurately. The need for accurate effort estimation in software industry is still a challenge. Artificial Neural Network models can be used for carrying out the effort estimations for developing a software project & this field of Soft Computing is suitable in effort estimations. The present paper is concerned with comparing the results of various artificial neural network models for predicting the software development effort estimation. The neural network models available in MATLAB neural network tools were used and the standard dataset as compiled by Lorenz et.al. was used in the present study. The results were analyzed using four different criterions MRE, MMRE, BRE and Pred. It is observed that the Generalised Regression Neural Network model provided better results.
  • Keywords
    mathematics computing; neural nets; regression analysis; software cost estimation; BRE; MATLAB neural network tools; MMRE; Pred; artificial neural network models; generalised regression neural network; soft computing; software development effort estimation; software development effort prediction; software project management; Accuracy; Artificial neural networks; Computational modeling; Estimation; Mathematical model; Programming; Software; Artificial Neural Network; Effort Estimation; Soft Computing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Emerging Trends and Applications in Computer Science (NCETACS), 2011 2nd National Conference on
  • Conference_Location
    Shillong
  • Print_ISBN
    978-1-4244-9578-8
  • Type

    conf

  • DOI
    10.1109/NCETACS.2011.5751391
  • Filename
    5751391