Building an Expert-based Web Effort Estimation Model using Bayesian Networks

OBJECTIVE - The objective of this paper is to describe a case study where Bayesian Networks (BNs) were used to construct an expert-based Web effort model. METHOD - We built a single-company BN model solely elicited from expert knowledge, where the domain expert was an experienced Web project manager from a small Web company in Auckland, New Zealand. This model was validated using data from eight past finished Web projects. RESULTS - The BN model has to date been successfully used to estimate effort for four Web projects, providing effort estimates superior to those based solely on expert opinion. CONCLUSIONS - Our results suggest that, at least for the Web Company that participated in this case study, the use of a model that allows the representation of uncertainty, inherent in effort estimation, can outperform expert-based estimates. Another five companies have also benefited from using Bayesian Networks, with very promising results.

Content

Author and article information

Contributors

Emilia Mendes

Carmel Pollino

Nile Mosley

Conference

Publication date: April 2009

Publication date (Print): April 2009

Pages: 1-10

Affiliations

[0001]Computer Science department

The University of Auckland

Private Bag, 92019, Auckland, NZ

[0002]The Fenner School of Environment and Society

The Australian National University

Canberra, Australia

[0003]MetriQ Ltd. (Research Division)

P.O. Box 837

Oneroa, Auckland, NZ

Article

DOI: 10.14236/ewic/EASE2009.5

SO-VID: f5942859-992d-4ee6-bfd1-9942806095c2

Copyright statement: © Emilia Mendes et al. Published by BCS Learning and Development Ltd. 13th International Conference on Evaluation and Assessment in Software Engineering (EASE), Durham 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: 13th International Conference on Evaluation and Assessment in Software Engineering (EASE)

Conference acronym: EASE

Conference number: 13

Conference location: Durham University, UK

Conference date: 20 - 21 April 2009

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/EASE2009.5

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

Building an Expert-based Web Effort Estimation Model using Bayesian Networks

Abstract