mlsanityData to 7 Oct 2026

LLM-based agents for automating the enhancement of user story quality: An early report

arXiv 2403.09442 ↗First posted 14 Mar 2024cs.SE cs.AIcited by 13

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

In agile software development, maintaining high-quality user stories is crucial, but also challenging. This study explores the use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams. We developed a reference model for an Autonomous LLM-based Agent System and implemented it at the company. The quality of user stories in the study and the effectiveness of these agents for user story quality improvement was assessed by 11 participants across six agile teams. Our findings demonstrate the potential of LLMs in improving user story quality, contributing to the research on AI role in agile development, and providing a practical example of the transformative impact of AI in an industry setting.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 10 of its 26 references that are in the map. First posted on arXiv 14 Mar 2024.

Builds on

10 of 26 references.

References · 10

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  4. 2022Automatic Chain of Thought Prompting in Large Language Modelscited by 684
  5. 2023Role-Play with Large Language Modelscited by 290
  6. 2021AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Promptscited by 265
  7. 2023The Troubling Emergence of Hallucination in Large Language Models -- An Extensive Definition, Quantification, and Prescriptive Remediationscited by 130
  8. 2023ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Designcited by 66
  9. 2023Investigating ChatGPT's Potential to Assist in Requirements Elicitation Processescited by 25
  10. 2023Generative Artificial Intelligence for Software Engineering -- A Research Agendacited by 23

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 6; 2026: 3.

13 papers.

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  1. 2024From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Futurecited by 85
  2. 2024Can LLMs "Reason" in Music? An Evaluation of LLMs' Capability of Music Understanding and Generationcited by 7
  3. 2025Facilitating Trustworthy Human-Agent Collaboration in LLM-based Multi-Agent System oriented Software Engineeringcited by 7
  4. 2024Experimenting with Multi-Agent Software Development: Towards a Unified Platformcited by 6
  5. 2025From Idea to Implementation: Evaluating the Influence of Large Language Models in Software Development -- An Opinion Papercited by 3
  6. 2024Getting Inspiration for Feature Elicitation: App Store- vs. LLM-based Approachcited by 1
  7. 2025AI and Agile Software Development: From Frustration to Success -- XP2025 Workshop Summarycited by 1
  8. 2025Multi-Agent LLMs as Ethics Advocates for AI-Based Systemscited by 1
  9. 2025Generating Privacy Stories From Software Documentationcited by 0
  10. 2025AI and Agile Software Development: A Research Roadmap from the XP2025 Workshopcited by 0
  11. 2026Comparing Large Language Models on Scrum Certification-Style Questions: Accuracy, Stability, and Error Patternscited by 0
  12. 2026Prompting GPT-5 on Scrum Certification Questions: An Empirical Accuracy Studycited by 0
1 more
  1. 2026Integrating AI into Requirements Quality Learning in Software Engineering Education: A TPACK-Guided Empirical Studycited by 0

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  1. 2025Cognitive Agents Powered by Large Language Models for Agile Software Project Management
  2. 2023Enhancing Trust in LLM-Based AI Automation Agents: New Considerations and Future Challenges
  3. 2026A Large-Scale Empirical Study of Quality Assurance Practices and Gaps in AI Agents
  4. 2024Assessing the Performance of Human-Capable LLMs -- Are LLMs Coming for Your Job?
  5. 2025An LLM-based multi-agent framework for agile effort estimation
  6. 2025ALMAS: an Autonomous LLM-based Multi-Agent Software Engineering Framework
  7. 2026Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report
  8. 2024Large Language Model-Based Agents for Software Engineering: A Survey