OBJECTIVE - The objective of this paper is to extend the work by Mendes [15], and to compare four techniques for Web effort estimation to identify which one provides best prediction accuracy.
METHOD - 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 - Results showed that predictions obtained using a BN were significantly superior to those using other techniques.
CONCLUSIONS - A model that incorporates the uncertainty inherent in effort estimation, can outperform other commonly used techniques, such as those used in this study.