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
Computational intelligence as an emerging paradigm of software engineering
SEKE '02 Proceedings of the 14th international conference on Software engineering and knowledge engineering
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
Prediction of Ordinal Classes Using Regression Trees
Fundamenta Informaticae - Intelligent Systems
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
Information and Software Technology
LMES: A localized multi-estimator model to estimate software development effort
Engineering Applications of Artificial Intelligence
Hi-index | 0.00 |
Background: It is widely recognized that software effort estimation is a regression problem. Model Tree (MT) is one of the Machine Learning based regression techniques that is useful for software effort estimation, but as other machine learning algorithms, the MT has a large space of configurations and requires to carefully setting its parameters. The choice of such parameters is a dataset dependent so no general guideline can govern this process which forms the motivation of this work. Aims: This study investigates the effect of using the most recent optimization algorithm called Bees algorithm to specify the optimal choice of MT parameters that fit a specific dataset and therefore improve prediction accuracy. Method: We used MT with optimal parameters identified by the Bees algorithm to construct software effort estimation model. The model has been validated over eight datasets come from two main sources: PROMISE and ISBSG. Also we used 3-Fold cross validation to empirically assess the prediction accuracies of different estimation models. As benchmark, results are also compared to those obtained with Stepwise Regression, Case-Based Reasoning and Multi-Layer Perceptron. Results: The results obtained from combination of MT and Bees algorithm are encouraging and outperforms other well-known estimation methods applied on employed datasets. They are also interesting enough to suggest the effectiveness of MT among the techniques that are suitable for effort estimation. Conclusions: The use of the Bees algorithm enabled us to automatically find optimal MT parameters that are required to construct accurate effort estimation model for each individual dataset.