Estimating Software Effort with Minimum Features Using Neural Functional Approximation

The aim of this study is to improve software effort estimation by incorporating straightforward mathematical principles and artificial neural network technique. Our process consists of three major steps. The first step concerns data preparation from each considered database. The second step is to reduce the number of given features by considering only those relevant ones. The final step is to transform the problem of estimating software effort to the problems of classification and functional approximation by using a feedforward neural network. Experimental data are taken from well-known public domains. The results are systematically compared with related prior works using only a few features so obtained, yet demonstrate that the proposed model yields satisfactory estimation accuracy based on MMRE and PRED measures.