Neural Network Based Effort Prediction Model for Maintenance Projects

  • V. Bharathi
  • Udaya Shastry
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 155)


One of the most critical requirements of High Maturity practices is the development of valid and usable prediction models (Process Performance Model, PPM) for quantitatively managing the outcome of a process. Multiple Regression Analysis is a tool generally used for model building. Over the last few years, Artificial Neural Networks have received a great deal of attention as prediction and classification tools. They have been applied successfully in diverse fields as data analysis tools. Here, we explore the applicability of neural network models for bug fix effort prediction in corrective maintenance project and present our findings


Process Performance Model Neural Networks 


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  1. 1.
    CMMI Development Team. Capability Maturity Model® Integrated Version 1.2, Software Engineering Institute (2001) Google Scholar
  2. 2.
    Automotive SIG. Automotive SPICE© Process Assessment Model (v2.5) and ProcessReference Model (V4.5) (2010) Google Scholar
  3. 3.
    Kitchenham, B., Pickard, L.: Towards a constructive quality model part ii, Statistical techniques for modeling software quality in the esprit request project. Software Engineering Journal 2(4), 114–126 (1987)CrossRefGoogle Scholar
  4. 4.
    Hampel, F.R., Ronchetti, E.M., Rousseeuw, P.J., Stahel, W.A.: Robust statistics. John Wiley & Sons, New York (1986)zbMATHGoogle Scholar
  5. 5.
    Namdar-Khojasteh, D., Shorafa, M., Omid, M., Fazeli-Shaghani, M.: Application of Artificial Neural Networks in Modeling Soil Solution Electrical Conductivity. Soil Science 175(9), 432–437 (2010)CrossRefGoogle Scholar
  6. 6.
    Intelligent Engineering Systems Through Artificial Neural Network. In: Proceedings of the Artificial Neural Networks in Engineering Conference, November 5-8, vol. 10. St Louis, Missouri (2000)Google Scholar
  7. 7.
    Krycha, K.A., Wagner, U.: Applications of artificial neural networks in management science: a survey. Journal of Retailing and Consumer Services 6(4), 185–203 (1999)CrossRefGoogle Scholar
  8. 8.
    Fukuda, T., Shibata, T.: Theory and applications of neural networks for industrial control systems. IEEE Transactions on Industrial Electronics 39(6), 472–489 (1992)CrossRefGoogle Scholar
  9. 9.
    Sarkar, S., Sindhgatta, R., Pooloth, K.: A Collaborative Platform for Application Knowledge Management in Software Maintenance Projects, Compute 2008, January 18-20, 2008, Bangalore, Karnataka, India ©. ACM, New York (2008) ISBN 978-1-59593-950-0 /08/01Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • V. Bharathi
    • 1
  • Udaya Shastry
    • 1
  1. 1.Wipro TechnologiesBangaloreIndia

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