Skip to content
Log in
Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
Review

Evaluation of Software Effort Estimation Methods for Machine learning techniques

Montaser Fadulalla Ahmed Adam Haroun Abdalla Eissa
· 0 citations · 21 references

TL;DR

A review of deferent machine learning methods that are using for effort estimation like regression models, decision trees, random forest, neural networks, and then evaluate this models based on performance criteria such as MAE (Mean Absolute Error) and R 2 Score.

Semantic Scholar
View source

Similar papers

Review Open access Jul 2026

Artificial Intelligence Techniques to Enhance Cost, Effort, and Schedule Estimates in Software Development Projects: A Systematic Literature Review

The findings indicate that techniques such as artificial neural networks, optimization algorithms, machine learning models, and hybrid approaches consistently yield improvements in estimation accuracy, with average error reductions reported in the literature ranging approximately from 15% to 30% when compared with trad...

Rodolfo Barbosa Santos, L. E. G. Martins · 0 citations
#small language model Open access Sep 2026

Comparative Benchmark of Eleven Regression Models for Software Effort Estimation on a COCOMO-Like Dataset

This study develops a comparative benchmark for software effort estimation using the benchmark suite implemented in Python and the result package generated by that suite. Eleven regressors were compared under a leakage-safe protocol on a COCOMO-like dataset of 62 projects and 20 numeric predictors, with 49 projects res...

Jaime Aguilar-Ortiz, Víctor M. Zamudio-García, Marcos Yamir Gómez-Ramos et al. · 0 citations
Open access Sep 2026

Comparative Analysis of Ensemble Learning Methods for Software Reliability Prediction

The results demonstrate that ensemble methods provide superior performance in identifying reliability levels, and which classification method is most suitable for predicting software reliability based on code metrics such as Cyclomatic Complexity and Halstead Volume.

Nadir Subaşı, Ö. Özer · 0 citations
Open access Aug 2026

A Robust Heterogeneous Ensemble Framework for Software Cost Estimation with Prediction Uncertainty Quantification

This paper presents a method using an uncertainty-aware heterogeneous ensemble of 100 bootstrap-trained base learners, which jointly produce point predictions and prediction intervals using a robust trimmed mean, with effort modeled on the log scale.

H. Sharif, Tara Nawzad Ahmad Al Attar, D. Rashid · 0 citations
Open access Jul 2026

Benchmarking Machine Learning Models for Software Project Schedule Overrun Prediction

Considering both prediction error and interpretability, Gradient Boosting Machine provided the strongest overall balance and represents a practical candidate for early-warning systems, risk-based project prioritization, schedule-recovery planning, and portfolio-level decision support.

Abdelkarim Ramzy Sweilem, Hala Jamil Abdoh, Ahmad Jamil Mohammad Abdoh · 0 citations
Aug 2026

Precision Software Defect Prediction Using Novel Machine Learning Approaches

This study aims to improve software defect prediction five publicly available NASA datasets by using Random Forest and Classification Network to achieve higher defect prediction accuracy compared to methods without feature selection (WOFS) and to get matrix problem the authors use Classification Network.

S. G., Santosh Santosh · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.