Empirical evaluation of analogy-x for software cost estimation
Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement
Stable rankings for different effort models
Automated Software Engineering
Adaptive ridge regression system for software cost estimating on multi-collinear datasets
Journal of Systems and Software
Recent methods for software effort estimation by analogy
ACM SIGSOFT Software Engineering Notes
An empirical evaluation of outlier deletion methods for analogy-based cost estimation
Proceedings of the 7th International Conference on Predictive Models in Software Engineering
Systematic literature review of machine learning based software development effort estimation models
Information and Software Technology
A replicated assessment and comparison of adaptation techniques for analogy-based effort estimation
Empirical Software Engineering
On the dataset shift problem in software engineering prediction models
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
How to treat timing information for software effort estimation?
Proceedings of the 2013 International Conference on Software and System Process
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
A PSO-based model to increase the accuracy of software development effort estimation
Software Quality Control
Finding conclusion stability for selecting the best effort predictor in software effort estimation
Automated Software Engineering
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Abstract Data-intensive analogy has been proposed as a means of software cost estimation as an alternative to other data intensive methods such as linear regression. Unfortunately, there are drawbacks to the method. There is no mechanism to assess its appropriateness for a specific dataset. In addition, heuristic algorithms are necessary to select the best set of variables and identify abnormal project cases. We introduce a solution to these problems based upon the use of the Mantel correlation randomization test called Analogy-X. We use the strength of correlation between the distance matrix of project features and the distance matrix of known effort values of the dataset. The method is demonstrated using the Desharnais dataset and two random datasets, showing (1) the use of Mantel's correlation to identify whether analogy is appropriate, (2) a stepwise procedure for feature selection, as well as (3) the use of a leverage statistic for sensitivity analysis that detects abnormal data points. Analogy-X, thus, provides a sound statistical basis for analogy, removes the need for heuristic search and greatly improves its algorithmic performance.