Synthesizing Statistical Knowledge from Incomplete Mixed-Mode Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Software engineering metrics and models
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Probabilistic reasoning in intelligent systems: networks of plausible inference
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The EM algorithm for graphical association models with missing data
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Introduction to Bayesian Networks
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Software Measurement: Uncertainty and Causal Modeling
IEEE Software
Building Probabilistic Networks: 'Where Do the Numbers Come From?' Guest Editors' Introduction
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A Comparative Study of Cost Estimation Models for Web Hypermedia Applications
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An Empirical Study on the Design Effort of Web Applications
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Cost estimation for web applications
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Bayesian Artificial Intelligence
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ISESE '03 Proceedings of the 2003 International Symposium on Empirical Software Engineering
Estimating the Design Effort of Web Applications
METRICS '03 Proceedings of the 9th International Symposium on Software Metrics
Early Web Size Measures and Effort Prediction for Web Costimation
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IEEE Transactions on Software Engineering
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Modeling Dependable Systems using Hybrid Bayesian Networks
ARES '06 Proceedings of the First International Conference on Availability, Reliability and Security
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ICWE '06 Proceedings of the 6th international conference on Web engineering
Network engineering for complex belief networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Numbers in multi-relational data mining
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
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AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
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
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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.