A Comparison of Techniques for Web Effort Estimation

The objective of this paper is to extend the work by Mendes (2007), and to compare four techniques for Web effort estimation to identify which one provides best prediction accuracy. We employed four effort estimation techniques - Bayesian networks (BN), forward stepwise regression (SWR), case-based reasoning (CBR) and Classification and regression trees (CART) to obtain effort estimates. The dataset employed was of 150 Web projects from the Tukutuku dataset. Results showed that predictions obtained using a BN were significantly superior to those using other techniques. A model that incorporates the uncertainty inherent in effort estimation, can outperform other commonly used techniques, such as those used in this study.