Volume 11, Number 3

Ensemble Regression Models for Software Development Effort Estimation: A Comparative Study


Halcyon D. P. Carvalho, Marília N. C. A. Lima, Wylliams B. Santos and Roberta A. de A.Fagunde, University of Pernambuco, Brazil


As demand for computer software continually increases, software scope and complexity become higher than ever. The software industry is in real need of accurate estimates of the project under development. Software development effort estimation is one of the main processes in software project management. However, overestimation and underestimation may cause the software industry loses. This study determines which technique has better effort prediction accuracy and propose combined techniques that could provide better estimates. Eight different ensemble models to estimate effort with Ensemble Models were compared with each other base on the predictive accuracy on the Mean Absolute Residual (MAR) criterion and statistical tests. The results have indicated that the proposed ensemble models, besides delivering high efficiency in contrast to its counterparts, and produces the best responses for software project effort estimation. Therefore, the proposed ensemble models in this study will help the project managers working with development quality software.


Ensemble Models, Bagging, Stacking, Prediction, Machine Learning, Effort Estimation, Project Management.