Effort estimation modeling techniques: a case study for web applications
ICWE '06 Proceedings of the 6th international conference on Web engineering
Improving analogy software effort estimation using fuzzy feature subset selection algorithm
Proceedings of the 4th international workshop on Predictor models in software engineering
The use of a Bayesian network for web effort estimation
ICWE'07 Proceedings of the 7th international conference on Web engineering
Systematic literature review of machine learning based software development effort estimation models
Information and Software Technology
Building an expert-based web effort estimation model using bayesian networks
EASE'09 Proceedings of the 13th international conference on Evaluation and Assessment in Software Engineering
Predicting web development effort using a bayesian network
EASE'07 Proceedings of the 11th international conference on Evaluation and Assessment in Software Engineering
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To date studies using CBR for Web hypermedia effort prediction have not applied adaptation rules to adjust effort according to a given criterion. In addition, when applyingn-fold cross-validation, their analysis has been limited to a maximum of three training sets, which according to recent studies, may lead to untrustworthy results.This paper has therefore two objectives. The first is to further investigate the use of CBR for Web hypermedia effort prediction by comparing the prediction accuracy of eight CBR techniques, of which three have previously been compared. The second objective is to compare the prediction accuracy of the best CBR technique against stepwise regression, using a twenty-fold cross-validation. All prediction accuracies were measured using MeanMagnitude of Relative Error (MMRE), Median Magnitude of Relative Error, Prediction at level l (l=25%), and boxplots of the residuals.One dataset was used in the estimation process and, according to all measures of prediction accuracy, stepwise regression showed the best prediction accuracy.