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Neural network models for software development effort estimation: a comparative study

  • Predictive Analytics Using Machine Learning
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Abstract

Software development effort estimation (SDEE) is one of the main tasks in software project management. It is crucial for a project manager to efficiently predict the effort or cost of a software project in a bidding process, since overestimation will lead to bidding loss and underestimation will cause the company to lose money. Several SDEE models exist; machine learning models, especially neural network models, are among the most prominent in the field. In this study, four different neural network models—multilayer perceptron, general regression neural network, radial basis function neural network, and cascade correlation neural network—are compared with each other based on: (1) predictive accuracy centred on the mean absolute error criterion, (2) whether such a model tends to overestimate or underestimate, and (3) how each model classifies the importance of its inputs. Industrial datasets from the International Software Benchmarking Standards Group (ISBSG) are used to train and validate the four models. The main ISBSG dataset was filtered and then divided into five datasets based on the productivity value of each project. Results show that the four models tend to overestimate in 80 % of the datasets, and the significance of the model inputs varies based on the selected model. Furthermore, the cascade correlation neural network outperforms the other three models in the majority of the datasets constructed on the mean absolute residual criterion.

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Notes

  1. The terms software cost and software effort are used interchangeably in this study.

  2. The terms software cost estimation and software cost prediction are used interchangeably in this study.

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Acknowledgments

Ali Bou Nassif would like to thank the University of Sharjah for supporting this research. Luiz Fernando Capretz, and Danny Ho would like to thank the Natural Sciences and Engineering Research Council of Canada (NSERC) for their support of this work through a Discovery Grant-Team.

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Correspondence to Ali Bou Nassif.

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Nassif, A.B., Azzeh, M., Capretz, L.F. et al. Neural network models for software development effort estimation: a comparative study. Neural Comput & Applic 27, 2369–2381 (2016). https://doi.org/10.1007/s00521-015-2127-1

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