Predicting Software Metrics at Design Time
PROFES '08 Proceedings of the 9th international conference on Product-Focused Software Process Improvement
State of the practice in software effort estimation: a survey and literature review
CEE-SET'08 Proceedings of the Third IFIP TC 2 Central and East European conference on Software engineering techniques
Systematic literature review of machine learning based software development effort estimation models
Information and Software Technology
Building an expert-based web effort estimation model using bayesian networks
EASE'09 Proceedings of the 13th international conference on Evaluation and Assessment in Software Engineering
Proceedings of the 34th International Conference on Software Engineering
A systematic review of web resource estimation
Proceedings of the 8th International Conference on Predictive Models in Software Engineering
The role of systematic reviews in identifying the state of the art in web resource estimation
Proceedings of the 2nd international workshop on Evidential assessment of software technologies
Discretization methods for NBC in effort estimation: an empirical comparison based on ISBSG projects
Proceedings of the ACM-IEEE international symposium on Empirical software engineering and measurement
Realising web effort estimation: a qualitative investigation
Proceedings of the 17th International Conference on Evaluation and Assessment in Software Engineering
Using CBR and CART to predict maintainability of relational database-driven software applications
Proceedings of the 17th International Conference on Evaluation and Assessment in Software Engineering
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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.