mlsanityData to 7 Oct 2026

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

arXiv 2404.01558 ↗First posted 2 Apr 2024cs.SE cs.AIcited by 7

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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.

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

Builds on

17 of 39 references.

References · 17

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  4. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  5. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
  6. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  7. 2023Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generationcited by 1,332
  8. 2024Hallucination is Inevitable: An Innate Limitation of Large Language Modelscited by 329
  9. 2023LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examplescited by 177
  10. 2022Synchromesh: Reliable code generation from pre-trained language modelscited by 156
  11. 2023Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Modelscited by 152
  12. 2023Review of Large Vision Models and Visual Prompt Engineeringcited by 77
5 more
  1. 2023ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Designcited by 66
  2. 2024SynCode: LLM Generation with Grammar Augmentationcited by 66
  3. 2022Using Pre-Trained Models to Boost Code Review Automationcited by 63
  4. 2021Towards Automating Code Review Activitiescited by 54
  5. 2023Towards Human-Bot Collaborative Software Architecting with ChatGPTcited by 31

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 2; 2026: 2.

7 papers.