Neural network and statistical modeling of software development effort

Ruchi Shukla, Mukul Shukla, Tshilidzi Marwala

Research output: Contribution to journalArticle


Many modeling studies that aimed at providing an accurate relationship between the software project effort (or cost) and the involved cost drivers have been conducted for effectivemanagement of software projects. However, the derived models are only applicable for a specific project and its variables. In this chapter, we present the use of back-propagation neural network (NN) to model the software development (SD) effort of 18 SD NASA projects based on six cost drivers. The performance of the NN model was also compared with a multi-regression model and other models available in the literature. © Springer India 2014.
Original languageEnglish
Pages (from-to)189-198
JournalAdvances in Intelligent Systems and Computing
Early online date26 Feb 2014
Publication statusPublished - 2014

Bibliographical note

cited By 0; Conference of 2nd International Conference on Soft Computing for Problem Solving, SocProS 2012 ; Conference Date: 28 December 2012 Through 30 December 2012; Conference Code:116609


  • Backpropagation
  • Costs
  • NASA
  • Neural networks
  • Problem solving
  • Regression analysis
  • Soft computing
  • Software engineering, Back propagation neural networks
  • Effort Estimation
  • Multi-regression model
  • NASA projects
  • Regression
  • Software development effort
  • Software project
  • Statistical modeling, Software design

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