# User Story Tutor (UST) to Support Agile Software Developers

arXiv: https://arxiv.org/abs/2406.16259 · HTML: https://mlsanity.com/paper/2406.16259/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 48 references that are in the map.
- First posted on arXiv 24 Jun 2024.

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

- [DeepTriage: Exploring the Effectiveness of Deep Learning for Bug Triaging](https://mlsanity.com/paper/1801.01275/) (2018)
- [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)

## Built on by (most cited first, 2 of 2)

- [Goal2Story: A Multi-Agent Fleet based on Privately Enabled sLLMs for Impacting Mapping on Requirements Elicitation](https://mlsanity.com/paper/2503.13279/) (2025)
- [User eXperience Perception Insights Dataset (UXPID): Synthetic User Feedback from Public Industrial Forums](https://mlsanity.com/paper/2509.11777/) (2025)

## Abstract (arXiv)

User Stories record what must be built in projects that use agile practices. User Stories serve both to estimate effort, generally measured in Story Points, and to plan what should be done in a Sprint. Therefore, it is essential to train software engineers on how to create simple, easily readable, and comprehensive User Stories. For that reason, we designed, implemented, applied, and evaluated a web application called User Story Tutor (UST). UST checks the description of a given User Story for readability, and if needed, recommends appropriate practices for improvement. UST also estimates a User Story effort in Story Points using Machine Learning techniques. As such UST may support the continuing education of agile development teams when writing and reviewing User Stories. UST's ease of use was evaluated by 40 agile practitioners according to the Technology Acceptance Model (TAM) and AttrakDiff. The TAM evaluation averages were good in almost all considered variables. Application of the AttrakDiff evaluation framework produced similar good results. Apparently, UST can be used with good reliability. Applying UST to assist in the construction of User Stories is a viable technique that, at the very least, can be used by agile developments to complement and enhance current User Story creation.