The Community for Technology Leaders
2013 ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (2007)
Madrid, Spain
Sept. 20, 2007 to Sept. 21, 2007
ISSN: 1938-6451
ISBN: 0-7695-2886-4
pp: 334-343
Emilia Mendes , University of Auckland, New Zealand
ABSTRACT
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. <p>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.</p> <p>RESULTS - Results showed that predictions obtained using a BN were significantly superior to those using other techniques.</p> <p>CONCLUSIONS - A model that incorporates the uncertainty inherent in effort estimation, can outperform other commonly used techniques, such as those used in this study.</p>
INDEX TERMS
null
CITATION
Emilia Mendes, "A Comparison of Techniques for Web Effort Estimation", 2013 ACM / IEEE International Symposium on Empirical Software Engineering and Measurement, vol. 00, no. , pp. 334-343, 2007, doi:10.1109/ESEM.2007.14
93 ms
(Ver )