Selection of Software Estimation Models Based on Analysis of Randomization and Spread Parameters in Neural Networks

Neural networks (NN) have demonstrated to be useful for estimating software development effort. A NN can be classified depending of its architecture. A Feedforward neural network (FFNN) and a General Regression Neural Network (GRNN) have two kinds of architectures. A FFNN uses randomization to be trained, whereas a GRNN uses a spread parameter to the same goal. Randomization as well as the spread parameter has influence on the accuracy of the models when they are used for estimating the development effort of software projects. Hence, in this study, an analysis of accuracies is done based on executions of NN involving random numbers and spread values. This study used two separated samples, one of them for training and the other one for validating the models (219 and 132 projects respectively). All projects where developed applying development practices based on Personal Software Process (PSP). Results of this study suggest that an analysis of random and spread parameters should be considered in both training and validation processes for selecting the suitable neural network model.