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

• Main Authors: Carvalho, Halcyon D. P, Lima, Marília N. C. A, Santos, Wylliams B, Fagunde, Roberta A. de A
• Format: Journal Article
• Language: English
• Published: 03-07-2020
• Subjects: Computer Science - Artificial Intelligence; Computer Science - Software Engineering
• DOI: 10.48550/arxiv.2007.01719

International Journal of Software Engineering & Applications
(IJSEA), Vol.11, No.3, May 2020 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.