Applying support vector regression for web effort estimation using a cross-company dataset
ESEM '09 Proceedings of the 2009 3rd International Symposium on Empirical Software Engineering and Measurement
A systematic review of software maintainability prediction and metrics
ESEM '09 Proceedings of the 2009 3rd International Symposium on Empirical Software Engineering and Measurement
Using Support Vector Regression for Web Development Effort Estimation
IWSM '09 /Mensura '09 Proceedings of the International Conferences on Software Process and Product Measurement
How effective is Tabu search to configure support vector regression for effort estimation?
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Defect cost flow model: a Bayesian network for predicting defect correction effort
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Investigating the use of Support Vector Regression for web effort estimation
Empirical Software Engineering
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
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Proceedings of the ACM-IEEE international symposium on Empirical software engineering and measurement
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The objective of this paper is to further investigate the use of Bayesian Networks (BN) for Web effort estimation when using a cross-company dataset. Four BNs were built; two automatically using the Hugin tool with two training sets; two using a structure elicited by a domain expert, with parameters obtained from automatically fitting the network to the same training sets used in the automated elicitation (hybrid models). The accuracy of all four models was measured using two validation sets, and point estimates. As a benchmark, the BN-based predictions were also compared to predictions obtained using Manual StepWise Regression (MSWR), and Case-Based Reasoning (CBR). The BN model generated using Hugin presented similar accuracy to CBR and Mean effort-based predictions. Our results suggest that Hybrid BN models can provide significantly superior prediction accuracy. However, good results also seem to depend on characteristics of the training and validation sets used.