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      Predicting Web Development Effort Using a Bayesian Network

      11th International Conference on Evaluation and Assessment in Software Engineering (EASE) (EASE)

      Evaluation and Assessment in Software Engineering (EASE)

      2-3 April 2007

      Web effort estimation, Bayesian networks, Forward stepwise regression, prediction accuracy

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          Abstract

          OBJECTIVE

          The objective of this paper is to investigate the use of a Bayesian Network (BN) for Web effort estimation.

          METHOD

          We built a BN automatically using the HUGIN tool and data on 120 Web projects from the Tukutuku database. In addition the BN model and node probability tables were also validated by a Web project manager from a well-established Web company in Rio de Janeiro (Brazil). The accuracy was measured using data on 30 projects (validation set), and point estimates (1-fold cross-validation using a 80%-20% split). The estimates obtained using the BN were also compared to estimates obtained using forward stepwise regression (SWR) as this is one of the most frequently used techniques for software and Web effort estimation.

          RESULTS

          Our results showed that BN-based predictions were better than previous predictions from Web-based cross-company models, and significantly better than predictions using SWR.

          CONCLUSIONS

          Our results suggest that, at least for the dataset used, the use of a model that allows the representation of uncertainty, inherent in effort estimation, can outperform other commonly used models, such as those built using multivariate regression techniques.

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          Most cited references 2

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          COBRA: a hybrid method for software cost estimation, benchmarking, and risk assessment

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            Hypertext: the next maintenance mountain

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              Author and article information

              Contributors
              Conference
              April 2007
              April 2007
              : 1-11
              Affiliations
              Computer Science department

              The University of Auckland

              Private Bag, 92019, Auckland, NZ
              Article
              10.14236/ewic/EASE2007.9
              © Emilia Mendes. Published by BCS Learning and Development Ltd. 11th International Conference on Evaluation and Assessment in Software Engineering (EASE), Keele University, UK

              This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

              11th International Conference on Evaluation and Assessment in Software Engineering (EASE)
              EASE
              11
              Keele University, UK
              2-3 April 2007
              Electronic Workshops in Computing (eWiC)
              Evaluation and Assessment in Software Engineering (EASE)
              Product
              Product Information: 1477-9358BCS Learning & Development
              Self URI (journal page): https://ewic.bcs.org/
              Categories
              Electronic Workshops in Computing

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