# Recent Advances in Software Effort Estimation using Machine Learning

arXiv: https://arxiv.org/abs/2303.03482 · HTML: https://mlsanity.com/paper/2303.03482/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 6 Mar 2023.

## Builds on (most cited first, 3 of 3)

- [A Survey on Deep Learning for Software Engineering](https://mlsanity.com/paper/2011.14597/) (2020)
- [A deep learning model for estimating story points](https://mlsanity.com/paper/1609.00489/) (2016)
- [A Versatile Dataset of Agile Open Source Software Projects](https://mlsanity.com/paper/2202.00979/) (2022)

## Abstract (arXiv)

An increasing number of software companies have already realized the importance of storing project-related data as valuable sources of information for training prediction models. Such kind of modeling opens the door for the implementation of tailored strategies to increase the accuracy in effort estimation of whole teams of engineers. In this article we review the most recent machine learning approaches used to estimate software development efforts for both, non-agile and agile methodologies. We analyze the benefits of adopting an agile methodology in terms of effort estimation possibilities, such as the modeling of programming patterns and misestimation patterns by individual engineers. We conclude with an analysis of current and future trends, regarding software effort estimation through data-driven predictive models.