Research Article Open Access

A HYBRID INTELLIGENT MODEL FOR SOFTWARE COST ESTIMATION

Wei Lin Du1, Luiz Fernando Capretz2, Ali Bou Nassif2 and Danny Ho1
  • 1 , Canada
  • 2 University of Western Ontario, Canada

Abstract

Accurate software development effort estimation is critical to the success of software projects. Although many techniques and algorithmic models have been developed and implemented by practitioners, accurate software development effort prediction is still a challenging endeavor in the field of software engineering, especially in handling uncertain and imprecise inputs and collinear characteristics. In this study, a hybrid intelligent model combining a neural network model integrated with fuzzy model (neuro-fuzzy model) has been used to improve the accuracy of estimating software cost. The performance of the proposed model is assessed by designing and conducting evaluation with published project and industrial data. Results have shown that the proposed model demonstrates the ability of improving the estimation accuracy by 18% based on the Mean Magnitude of Relative Error (MMRE) criterion.

Journal of Computer Science
Volume 9 No. 11, 2013, 1506-1513

DOI: https://doi.org/10.3844/jcssp.2013.1506.1513

Submitted On: 9 July 2013 Published On: 28 September 2013

How to Cite: Du, W. L., Capretz, L. F., Nassif, A. B. & Ho, D. (2013). A HYBRID INTELLIGENT MODEL FOR SOFTWARE COST ESTIMATION. Journal of Computer Science, 9(11), 1506-1513. https://doi.org/10.3844/jcssp.2013.1506.1513

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Keywords

  • Hybrid Intelligent Model
  • Software Cost Estimation
  • Neuro-Fuzzy
  • Predictive Model