A principled evaluation of ensembles of learning machines for software effort estimation
Proceedings of the 7th International Conference on Predictive Models in Software Engineering
Local bias and its impacts on the performance of parametric estimation models
Proceedings of the 7th International Conference on Predictive Models in Software Engineering
The inductive software engineering manifesto: principles for industrial data mining
Proceedings of the International Workshop on Machine Learning Technologies in Software Engineering
A replicated assessment and comparison of adaptation techniques for analogy-based effort estimation
Empirical Software Engineering
Local vs. global models for effort estimation and defect prediction
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Web effort estimation: the value of cross-company data set compared to single-company data set
Proceedings of the 8th International Conference on Predictive Models in Software Engineering
Alternative methods using similarities in software effort estimation
Proceedings of the 8th International Conference on Predictive Models in Software Engineering
Size doesn't matter?: on the value of software size features for effort estimation
Proceedings of the 8th International Conference on Predictive Models in Software Engineering
Software effort models should be assessed via leave-one-out validation
Journal of Systems and Software
Data science for software engineering
Proceedings of the 2013 International Conference on Software Engineering
Building a second opinion: learning cross-company data
Proceedings of the 9th International Conference on Predictive Models in Software Engineering
Software development cost estimation using similarity difference between software attributes
Proceedings of the 2013 International Conference on Information Systems and Design of Communication
On the value of outlier elimination on software effort estimation research
Empirical Software Engineering
LMES: A localized multi-estimator model to estimate software development effort
Engineering Applications of Artificial Intelligence
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Background: There are too many design options for software effort estimators. How can we best explore them all? Aim: We seek aspects on general principles of effort estimation that can guide the design of effort estimators. Method: We identified the essential assumption of analogy-based effort estimation, i.e., the immediate neighbors of a project offer stable conclusions about that project. We test that assumption by generating a binary tree of clusters of effort data and comparing the variance of supertrees versus smaller subtrees. Results: For 10 data sets (from Coc81, Nasa93, Desharnais, Albrecht, ISBSG, and data from Turkish companies), we found: 1) The estimation variance of cluster subtrees is usually larger than that of cluster supertrees; 2) if analogy is restricted to the cluster trees with lower variance, then effort estimates have a significantly lower error (measured using MRE, AR, and Pred(25) with a Wilcoxon test, 95 percent confidence, compared to nearest neighbor methods that use neighborhoods of a fixed size). Conclusion: Estimation by analogy can be significantly improved by a dynamic selection of nearest neighbors, using only the project data from regions with small variance.