Bayesian Network Models for Web Effort Prediction: A Comparative Study

Abstract

The objective of this paper is to compare, using a cross-company dataset, several Bayesian network (BN) models for Web effort estimation. Eight BNs were built; four automatically using Hugin and PowerSoft tools with two training sets, each with 130 Web projects from the Tukutuku database; four using a causal graph elicited by a domain expert, with parameters automatically fit using the same training sets used in the automated elicitation (hybrid models). Their accuracy was measured using two validation sets, each containing data on 65 projects, and point estimates. As a benchmark, the BN-based estimates were also compared to estimates obtained using manual stepwise regression (MSWR), case-based reasoning (CBR), mean- and median-based effort models. MSWR presented significantly better predictions than any of the BN models built herein, and in addition was the only technique to provide significantly superior predictions to a median-based effort model. This paper investigated data-driven and hybrid BN models using project data from the Tukutuku database. Our results suggest that the use of simpler models, such as the median effort, can outperform more complex models, such as BNs. In addition, MSWR seemed to be the only effective technique for Web effort estimation.