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

TL;DR

This paper investigates the use of large language models to automatically enhance user story quality in agile software development, demonstrating potential improvements and practical industry applications.

Contribution

It introduces a reference model for an LLM-based agent system and evaluates its effectiveness in real-world agile teams, a novel application in industry settings.

Findings

LLMs can improve user story quality effectively.

The implemented system was positively received by agile teams.

The study provides a practical example of AI's transformative role in industry.

Abstract

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.