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Software Effort Estimation Using a Neural Network Ensemble
Journal of Computer Information Systems (2013)
  • Dinesh R. Pai
  • Kevin McFall, Kennesaw State University
  • Girish H. Subramanian
Accurate software effort estimation is crucial for software consulting organizations to stay competitive in their software development costs and retain customers. Artificial Neural Network (ANN) is an effective tool to obtain accurate effort estimates. In this paper, software effort estimation models using Artificial Neural Network (ANN) ensembles and regression analysis are developed based on data collected from 163 software development projects. The main emphasis of the paper is in developing an effective experimental design to achieve superior effort estimation results. In addition, we compare the software effort estimation of ANNs and multiple regression analysis. We found two interesting results. First, variables other than size (function points) are not especially helpful in predicting software development effort. Second, a properly designed ANN ensemble significantly outperforms estimation using regression analysis and can achieve better effort estimate predictions.
  • artificial neural network,
  • resampling technique,
  • software development effort estimation,
  • regression
Publication Date
Citation Information
Dinesh R. Pai, Kevin McFall and Girish H. Subramanian. "Software Effort Estimation Using a Neural Network Ensemble" Journal of Computer Information Systems Vol. 53 Iss. 4 (2013) p. 49 - 58 ISSN: 2380-2057
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