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First International Symposium on Empirical Software Engineering and Measurement (ESEM 2007)
A Comparison of Techniques for Web Effort Estimation
Madrid, Spain
September 20-September 21
ISBN: 0-7695-2886-4
Emilia Mendes, University of Auckland, New Zealand
OBJECTIVE - The objective of this paper is to extend the work by Mendes [15], and to compare four techniques for Web effort estimation to identify which one provides best prediction accuracy.

METHOD - We employed four effort estimation techniques - Bayesian networks (BN), forward stepwise regression (SWR), case-based reasoning (CBR) and Classification and regression trees (CART) to obtain effort estimates. The dataset employed was of 150 Web projects from the Tukutuku dataset.

RESULTS - Results showed that predictions obtained using a BN were significantly superior to those using other techniques.

CONCLUSIONS - A model that incorporates the uncertainty inherent in effort estimation, can outperform other commonly used techniques, such as those used in this study.

Citation:
Emilia Mendes, "A Comparison of Techniques for Web Effort Estimation," esem, pp.334-343, First International Symposium on Empirical Software Engineering and Measurement (ESEM 2007), 2007
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