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
Using Support Vector Regression for Web Development Effort Estimation
IWSM '09 /Mensura '09 Proceedings of the International Conferences on Software Process and Product Measurement
Validating a size measure for effort estimation in model-driven Web development
Information Sciences: an International Journal
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
Investigating the use of Support Vector Regression for web effort estimation
Empirical Software Engineering
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Size represents one of the most important attribute of software products used to predict software development effort. In the past nine years, several measures have been proposed to estimate the size of Web applications, and it is important to determine which one is most effective to predict Web development effort. To this aim in this paper we report on an empirical analysis where, using data from 15 Web projects developed by a software company, we compare four sets of size measures, using two prediction techniques, namely Forward Stepwise Regression (SWR) and Case-Based Reasoning (CBR). All the measures provided good predictions in terms of MMRE, MdMRE, and Pred(0.25) statistics, for both SWR and CBR. Moreover, when using SWR, Length measures and Web Objects gave significant better results than Functional measures, however presented similar results to the Tukutuku measures. As for CBR, results did not show any significant differences amongst the four sets of size measures.