A Radial Basis Function Neural Network for Predicting the Effort of Software Projects Individually Developed in Laboratory Learning Environments

摘要

Prediction techniques have been applied for predicting dependent variables related to Higher Education students such as dropout, grades, course selection, and satisfaction. In this research, we propose a prediction technique for predicting the effort of software projects individually developed by graduate students. In accordance with the complexity of a software project, it can be developed among teams, by a team or even at individual level. The teaching and training about development effort prediction of software projects represents a concern in environments related to academy and industry because underprediction causes cost overruns, whereas overprediction often involves missed financial opportunities. Effort prediction techniques of individually developed projects have mainly been based on expert judgment or based on mathematical models. This research proposes the application of a mathematical model termed Radial Basis function Neural Network (RBFNN). The hypothesis to be tested is the following: effort prediction accuracy of a RBFNN is statistically better than that obtained from a Multiple Linear Regression (MLR). The projects were developed by following a disciplined development process in controlled environments. The RBFNN and MLR were trained from a data set of 328 projects developed by 82 students between the years 2005 and 2010, then, the models were tested using a data set of 116 projects developed by 29 students between the years 2011 and first semester of 2012. Results suggest that a RBFNN having as independent variables new and changed code, reused code and programming language experience of students can be used at the 95.0% confidence level for predicting the development effort of individual projects when they have been developed based upon a disciplined process in academic environments.