Software Development Effort Estimation in Academic Environments Applying a General Regression Neural Network Involving Size and People Factors

  • Cuauhtémoc López-Martín
  • Arturo Chavoya
  • M. E. Meda-Campaña
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6718)


In this research a general regression neural network (GRNN) was applied for estimating the development effort in software projects that have been developed in laboratory learning environments. The independent variables of the GRNN were two size measures as well as a developer measure. This GRNN was trained from a dataset of projects developed from the year 2005 to the year 2008 and then this GRNN was validated by estimating the effort of a new dataset integrated by projects developed from the year 2009 o the first months of the year 2010. Accuracy results from the GRNN model were compared with a statistical regression model. Results suggest that a GRNN could be used for estimating the development effort of software projects when two kinds of lines of code as well as the programming language experience of developers are used as independent variables.


Software engineering software effort estimation general regression neural network statistical regression programming language experience 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Cuauhtémoc López-Martín
    • 1
  • Arturo Chavoya
    • 1
  • M. E. Meda-Campaña
    • 1
  1. 1.Department of Information SystemsUniversity of GuadalajaraMéxico

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