# Automated User Story Generation with Test Case Specification Using Large Language Model

arXiv: https://arxiv.org/abs/2404.01558 · HTML: https://mlsanity.com/paper/2404.01558/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 39 references that are in the map.
- First posted on arXiv 2 Apr 2024.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation](https://mlsanity.com/paper/2305.01210/) (2023)
- [Hallucination is Inevitable: An Innate Limitation of Large Language Models](https://mlsanity.com/paper/2401.11817/) (2024)
- [LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples](https://mlsanity.com/paper/2310.01469/) (2023)
- [Synchromesh: Reliable code generation from pre-trained language models](https://mlsanity.com/paper/2201.11227/) (2022)
- [Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models](https://mlsanity.com/paper/2304.01852/) (2023)
- [Review of Large Vision Models and Visual Prompt Engineering](https://mlsanity.com/paper/2307.00855/) (2023)
- [SynCode: LLM Generation with Grammar Augmentation](https://mlsanity.com/paper/2403.01632/) (2024)
- [ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design](https://mlsanity.com/paper/2303.07839/) (2023)
- [Using Pre-Trained Models to Boost Code Review Automation](https://mlsanity.com/paper/2201.06850/) (2022)
- [Towards Automating Code Review Activities](https://mlsanity.com/paper/2101.02518/) (2021)
- [Towards Human-Bot Collaborative Software Architecting with ChatGPT](https://mlsanity.com/paper/2302.14600/) (2023)

## Built on by (most cited first, 7 of 7)

- [From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future](https://mlsanity.com/paper/2408.02479/) (2024)
- [Using LLMs in Software Requirements Specifications: An Empirical Evaluation](https://mlsanity.com/paper/2404.17842/) (2024)
- [Reverse Engineering User Stories from Code using Large Language Models](https://mlsanity.com/paper/2509.19587/) (2025)
- [System Test Case Design from Requirements Specifications: Insights and Challenges of Using ChatGPT](https://mlsanity.com/paper/2412.03693/) (2024)
- [A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards](https://mlsanity.com/paper/2505.13766/) (2025)
- [From Reviews to Requirements: Can LLMs Generate Human-Like User Stories?](https://mlsanity.com/paper/2603.28163/) (2026)
- [How Well Can AI Generate Backlogs from App Mockups?](https://mlsanity.com/paper/2607.22902/) (2026)

## Abstract (arXiv)

Modern Software Engineering era is moving fast with the assistance of artificial intelligence (AI), especially Large Language Models (LLM). Researchers have already started automating many parts of the software development workflow. Requirements Engineering (RE) is a crucial phase that begins the software development cycle through multiple discussions on a proposed scope of work documented in different forms. RE phase ends with a list of user-stories for each unit task identified through discussions and usually these are created and tracked on a project management tool such as Jira, AzurDev etc. In this research we developed a tool "GeneUS" using GPT-4.0 to automatically create user stories from requirements document which is the outcome of the RE phase. The output is provided in JSON format leaving the possibilities open for downstream integration to the popular project management tools. Analyzing requirements documents takes significant effort and multiple meetings with stakeholders. We believe, automating this process will certainly reduce additional load off the software engineers, and increase the productivity since they will be able to utilize their time on other prioritized tasks.