  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 80percent 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.
