Predicting Web Development Effort Using a Bayesian Network

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

Content

Author and article information

Contributors

Emilia Mendes

Conference

Publication date: April 2007

Publication date (Print): April 2007

Pages: 1-11

Affiliations

[0001]Computer Science department

The University of Auckland

Private Bag, 92019, Auckland, NZ

Article

DOI: 10.14236/ewic/EASE2007.9

SO-VID: 1d4e1740-4877-4591-abfa-bfa785811787

Copyright statement: © Emilia Mendes. Published by BCS Learning and Development Ltd. 11th International Conference on Evaluation and Assessment in Software Engineering (EASE), Keele University, UK

License:

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/

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

Conference acronym: EASE

Conference number: 11

Conference location: Keele University, UK

Conference date: 2-3 April 2007

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: Evaluation and Assessment in Software Engineering (EASE)

Product

Product Information: 1477-9358BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/EASE2007.9

Self URI (journal page): https://ewic.bcs.org/

Predicting Web Development Effort Using a Bayesian Network

Abstract