An empirical validation of software cost estimation models
Communications of the ACM
Software engineering metrics and models
Software engineering metrics and models
Estimating Software Project Effort Using Analogies
IEEE Transactions on Software Engineering
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Multicriteria Optimization
A comparative study of attribute weighting heuristics for effort estimation by analogy
Proceedings of the 2006 ACM/IEEE international symposium on Empirical software engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A flexible method for software effort estimation by analogy
Empirical Software Engineering
Selecting Best Practices for Effort Estimation
IEEE Transactions on Software Engineering
Decision Support Analysis for Software Effort Estimation by Analogy
ICSEW '07 Proceedings of the 29th International Conference on Software Engineering Workshops
Analysis of attribute weighting heuristics for analogy-based software effort estimation method AQUA+
Empirical Software Engineering
Software effort estimation based on weighted fuzzy grey relational analysis
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
Fuzzy grey relational analysis for software effort estimation
Empirical Software Engineering
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The quality of results from a predictor model depends on the proper customization of the parameters of the model. For Estimation by Analogy (EBA), the impact of the parameter "Attribute weighting technique" has been shown by several authors. The decision problem "Which attribute weighting technique is preferable for EBA in which situation?" is considered in this paper from the perspective of multi-criteria decision analysis (MCDA). The empirical results are given for the EBA method AQUA+. More specifically, two MCDA techniques, ELECTRE and Pareto-optimality are applied. Three evaluation criteria MMRE (Mean Magnitude of Relative Error), Pred (Prediction at certain accuracy level), and Strength are considered. We discuss the insights gained from this more in-depth decision analysis for the stated decision problem.