Predictive Regression Models of Machine Learning for Effort Estimation in Software Teams: An Experimental Study

Wilamis K. N. da Silva, Bernan R. Nascimento, Péricles Miranda, Emanuel P. Vicente

2025

Abstract

Estimating the effort required by software teams remains complex, with numerous techniques employed over the years. This study presents a controlled experiment in which machine learning techniques were applied to predict software team effort. Seven regression techniques were tested using eight PROMISE datasets, with their performance evaluated across five metrics. The findings indicate that the XGBoost technique yielded the best results. These results suggest that XGBoost is highly competitive compared to other established techniques in the field. The paper proved to lay the foundation to guide future researchers in conducting research in the field of software team effort estimation.

Paper Citation

in Harvard Style

Silva W., Nascimento B., Miranda P. and Vicente E. (2025). Predictive Regression Models of Machine Learning for Effort Estimation in Software Teams: An Experimental Study. In Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 2: ICEIS; ISBN 978-989-758-749-8, SciTePress, pages 219-226. DOI: 10.5220/0013284800003929

in Bibtex Style

@conference{iceis25,
author={Wilamis Silva and Bernan Nascimento and Péricles Miranda and Emanuel Vicente},
title={Predictive Regression Models of Machine Learning for Effort Estimation in Software Teams: An Experimental Study},
booktitle={Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 2: ICEIS},
year={2025},
pages={219-226},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013284800003929},
isbn={978-989-758-749-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 2: ICEIS
TI - Predictive Regression Models of Machine Learning for Effort Estimation in Software Teams: An Experimental Study
SN - 978-989-758-749-8
AU - Silva W.
AU - Nascimento B.
AU - Miranda P.
AU - Vicente E.
PY - 2025
SP - 219
EP - 226
DO - 10.5220/0013284800003929
PB - SciTePress