# A deep learning model for estimating story points

arXiv: https://arxiv.org/abs/1609.00489 · HTML: https://mlsanity.com/paper/1609.00489/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2016.

## Built on by (most cited first, 10 of 10)

- [Keeping Deep Learning Models in Check: A History-Based Approach to Mitigate Overfitting](https://mlsanity.com/paper/2401.10359/) (2024)
- [An LLM-based multi-agent framework for agile effort estimation](https://mlsanity.com/paper/2509.14483/) (2025)
- [The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering Tasks: A Systematic Mapping Review](https://mlsanity.com/paper/2206.00421/) (2022)
- [Follow Your Nose -- Which Code Smells are Worth Chasing?](https://mlsanity.com/paper/2103.01861/) (2021)
- [Agile Effort Estimation: Have We Solved the Problem Yet? Insights From A Replication Study](https://mlsanity.com/paper/2201.05401/) (2022)
- [Heterogeneous Graph Neural Networks for Software Effort Estimation](https://mlsanity.com/paper/2206.11023/) (2022)
- [User Story Tutor (UST) to Support Agile Software Developers](https://mlsanity.com/paper/2406.16259/) (2024)
- [Story Point Effort Estimation by Text Level Graph Neural Network](https://mlsanity.com/paper/2203.03062/) (2022)
- [ARLO: A Tailorable Approach for Transforming Natural Language Software Requirements into Architecture using LLMs](https://mlsanity.com/paper/2504.06143/) (2025)
- [Recent Advances in Software Effort Estimation using Machine Learning](https://mlsanity.com/paper/2303.03482/) (2023)

## Abstract (arXiv)

Although there has been substantial research in software analytics for effort estimation in traditional software projects, little work has been done for estimation in agile projects, especially estimating user stories or issues. Story points are the most common unit of measure used for estimating the effort involved in implementing a user story or resolving an issue. In this paper, we offer for the first time a comprehensive dataset for story points-based estimation that contains 23,313 issues from 16 open source projects. We also propose a prediction model for estimating story points based on a novel combination of two powerful deep learning architectures: long short-term memory and recurrent highway network. Our prediction system is end-to-end trainable from raw input data to prediction outcomes without any manual feature engineering. An empirical evaluation demonstrates that our approach consistently outperforms three common effort estimation baselines and two alternatives in both Mean Absolute Error and the Standardized Accuracy.