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.

Literature shows that over the years numerous techniques for estimating development effort have been suggested, derived from late project measures. However, to the successful management of software projects, estimates are necessary throughout the whole development life cycle. The objective is twofold. First, we describe the application of case-based reasoning (CBR) for estimating Web hypermedia development effort using measures collected at different stages in the development cycle. Second, we compare the prediction accuracy of those measures, obtained using different CBR configurations. Contrary to the expected, late measures did not show statistically significant better predictions than early measures.

Over the years software engineering researchers have suggested numerous techniques for estimating development effort. These techniques have been classified mainly as algorithmic, machine learning and expert judgement. Several studies have compared the prediction accuracy of those techniques, with emphasis placed on linear regression, stepwise regression, and Case-based Reasoning (CBR). To date no converging results have been obtained and we believe they may be influenced by the use of the same CBR configuration.The objective of this paper is twofold. First, to describe the application of case-based reasoning for estimating the effort for developing Web hypermedia applications. Second, comparing the prediction accuracy of different CBR configurations, using two Web hypermedia datasets.Results show that for both datasets the best estimations were obtained with weighted Euclidean distance, using either one analogy (dataset 1) or 3 analogies (dataset 2). We suggest therefore that case-based reasoning is a candidate technique for effort estimation and, with the aid of an automated environment, can be applied to Web hypermedia development effort prediction.

Hypermedia engineering constitutes the employment of an engineering approach to the development of hypermedia applications. Its main teaching objectives are for students to learn what an engineering approach means and how measurement can be applied.This paper presents the application of the Cognitive Flexibility Theory as an instructional theory to teach Hypermedia Engineering principles.Early results have shown that students presented a greater learning variability (suggested by their exam marks) when exposed to the CFT as a teaching practice, compared to conventional methods.