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

TL;DR

This paper compares eight ensemble regression models for software effort estimation, demonstrating that the proposed ensemble techniques outperform individual models in predictive accuracy, aiding project managers in delivering quality software on time and within budget.

Contribution

The study introduces and evaluates new ensemble regression models that improve effort estimation accuracy over existing methods in software project management.

Findings

Ensemble models achieved lower Mean Absolute Residual (MAR) than individual models.

Proposed ensemble techniques showed statistically significant improvements in prediction accuracy.

Enhanced effort estimation can reduce project overruns and improve resource allocation.

Abstract

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.