Electrical and Computer Engineering Publications

Document Type

Article

Publication Date

11-2016

Volume

27

Issue

8

Journal

Neural Computing & Applications

First Page

2369

URL with Digital Object Identifier

http://dx.doi.org/10.1007/s00521-015-2127-1

Last Page

2381

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 centered 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.

Notes

http://dx.doi.org/10.1007/s00521-015-2127-1

Citation of this paper:

Nassif A.B., Azzeh, M., Capretz L.F. and Ho D. Neural Network Models for Software Development Effort Estimation: A Comparative Study, Neural Computing & Applications, Volume 27, Issue 8, pp. 2369-2381, DOI: 10.1007/s00521-015-2127-1, Springer, November 2016.

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