Estimating Software Project Effort Using Analogies
IEEE Transactions on Software Engineering
An assessment and comparison of common software cost estimation modeling techniques
Proceedings of the 21st international conference on Software engineering
Comparing Software Prediction Techniques Using Simulation
IEEE Transactions on Software Engineering - Special section on the seventh international software metrics symposium
An Empirical Study of Analogy-based Software Effort Estimation
Empirical Software Engineering
A Comparative Study of Cost Estimation Models for Web Hypermedia Applications
Empirical Software Engineering
A Replicated Assessment of the Use of Adaptation Rules to Improve Web Cost Estimation
ISESE '03 Proceedings of the 2003 International Symposium on Empirical Software Engineering
Software effort estimation by analogy and "regression toward the mean"
Journal of Systems and Software - Special issue: Best papers on Software Engineering from the SEKE'01 Conference
METRICS '05 Proceedings of the 11th IEEE International Software Metrics Symposium
Optimal Project Feature Weights in Analogy-Based Cost Estimation: Improvement and Limitations
IEEE Transactions on Software Engineering
The adjusted analogy-based software effort estimation based on similarity distances
Journal of Systems and Software
A flexible method for software effort estimation by analogy
Empirical Software Engineering
Selecting Best Practices for Effort Estimation
IEEE Transactions on Software Engineering
A study of the non-linear adjustment for analogy based software cost estimation
Empirical Software Engineering
Fuzzy grey relational analysis for software effort estimation
Empirical Software Engineering
An empirical analysis of linear adaptation techniques for case-based prediction
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
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Case-Based Reasoning (CBR) has achieved a considerable interest from researchers for solving non-trivial or ill-defined problems such as those encountered by project managers including support for software project management in predictions and lesson learned. Software effort estimation is the key factor for successful software project management. In particular, the use of CBR for effort estimation was favored over regression and other machine learning techniques due to its performance in generating reliable estimates. However, this method was subject to variety of design options which therefore has strong impact on the prediction accuracy. Selection of CBR adjustment method and deciding on the number of analogies are such two important decisions for generating accurate and reliable estimates. This paper proposed a new method to adjust the retrieved project efforts and find optimal number of analogies by using Bees optimization algorithm. The Bees algorithm will be used to search for the best number of analogies and features coefficient values that will be used to reduce estimates errors. Results obtained are promising and the proposed method could form a useful extension for Case-based effort prediction model.