A Pattern Recognition Approach for Software Engineering Data Analysis
IEEE Transactions on Software Engineering - Special issue on software measurement principles, techniques, and environments
Measures for Excellence: Reliable Software on Time, within Budget
Measures for Excellence: Reliable Software on Time, within Budget
Software development cost estimation approaches – A survey
Annals of Software Engineering
Machine Learning
Neural Network Approach for Software Cost Estimation
ITCC '05 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II - Volume 02
A Systematic Review of Software Development Cost Estimation Studies
IEEE Transactions on Software Engineering
Cognitive Limits of Software Cost Estimation
ESEM '07 Proceedings of the First International Symposium on Empirical Software Engineering and Measurement
Towards an early software estimation using log-linear regression and a multilayer perceptron model
Journal of Systems and Software
Extraction of fuzzy rules from fuzzy decision trees: An axiomatic fuzzy sets (AFS) approach
Data & Knowledge Engineering
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This paper addresses the issue of software cost estimation through fuzzy decision trees, aiming at acquiring accurate and reliable effort estimates for project resource allocation and control. Two algorithms, namely CHAID and CART, are applied on empirical software cost data recorded in the ISBSG repository. Approximately 1000 project data records are selected for analysis and experimentation, with fuzzy decision trees instances being generated and evaluated based on prediction accuracy. The set of association rules extracted is used for providing mean effort value ranges. The experimental results suggest that the proposed approach may provide accurate cost predictions in terms of effort. In addition, there is strong evidence that the fuzzy transformation of cost drivers contribute to enhancing the estimation process.