# Leveraging AI for Enhanced Software Effort Estimation: A Comprehensive Study and Framework Proposal

arXiv: https://arxiv.org/abs/2402.05484 · HTML: https://mlsanity.com/paper/2402.05484/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 11 references that are in the map.
- First posted on arXiv 8 Feb 2024.

## Builds on (most cited first, 1 of 1)

- [Agile Effort Estimation: Have We Solved the Problem Yet? Insights From A Replication Study](https://mlsanity.com/paper/2201.05401/) (2022)

## Abstract (arXiv)

This paper presents an extensive study on the application of AI techniques for software effort estimation in the past five years from 2017 to 2023. By overcoming the limitations of traditional methods, the study aims to improve accuracy and reliability. Through performance evaluation and comparison with diverse Machine Learning models, including Artificial Neural Network (ANN), Support Vector Machine (SVM), Linear Regression, Random Forest and other techniques, the most effective method is identified. The proposed AI-based framework holds the potential to enhance project planning and resource allocation, contributing to the research area of software project effort estimation.