AI-Based Approaches for Software Tasks Effort Estimation: A Systematic Review of Methods and Trends

Bruno Budel Rossi, Lisandra Fontoura

2025

Abstract

Accurate measurement of task effort in software projects is essential for effective management and project success in software engineering. Conventional methods often face limitations in both accuracy and their ability to adapt to the complexities of contemporary projects. This systematic analysis examines the use of ensemble learning methods and other artificial intelligence strategies for estimating task effort in software projects. The review focuses on methods that employ machine learning, neural networks, large language models, and natural language processing to improve the accuracy of effort estimation. The use of expert opinion is also discussed, along with the metrics utilized in task effort estimation. A total of 826 empirical and theoretical studies were analyzed using a comprehensive search across the ACM Digital Library, IEEE Digital Library, ScienceDirect, and Scopus databases, with 66 studies selected for further analysis. The results highlight the effectiveness, current trends, and benefits of these techniques, suggesting that adopting AI could lead to substantial improvements in effort estimation accuracy and more efficient software project management.

Paper Citation

in Harvard Style

Rossi B. and Fontoura L. (2025). AI-Based Approaches for Software Tasks Effort Estimation: A Systematic Review of Methods and Trends. In Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 2: ICEIS; ISBN 978-989-758-749-8, SciTePress, pages 144-151. DOI: 10.5220/0013218200003929

in Bibtex Style

@conference{iceis25,
author={Bruno Rossi and Lisandra Fontoura},
title={AI-Based Approaches for Software Tasks Effort Estimation: A Systematic Review of Methods and Trends},
booktitle={Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 2: ICEIS},
year={2025},
pages={144-151},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013218200003929},
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 - AI-Based Approaches for Software Tasks Effort Estimation: A Systematic Review of Methods and Trends
SN - 978-989-758-749-8
AU - Rossi B.
AU - Fontoura L.
PY - 2025
SP - 144
EP - 151
DO - 10.5220/0013218200003929
PB - SciTePress