SimAC : Simulating Agile Collaboration to Generate Acceptance Criteria in User Story Elaboration

In agile requirements engineering, Generating Acceptance Criteria (GAC) to elaborate user stories plays a pivotal role in the sprint planning phase, which provides a reference for delivering functional solutions. GAC requires extensive collaboration and human involvement. However, the lack of labeled datasets tailored for User Story attached with Acceptance Criteria (US-AC) poses significant challenges for supervised learning techniques attempting to automate this process. Recent advancements in Large Language Models (LLMs) have showcased their remarkable text-generation capabilities, bypassing the need for supervised fine-tuning. Consequently, LLMs offer the potential to overcome the above challenge. Motivated by this, we propose SimAC, a framework leveraging LLMs to simulate agile collaboration, with three distinct role groups: requirement analyst, quality analyst, and others. Initiated by role-based prompts, LLMs act in these roles sequentially, following a create-update-update paradigm in GAC. Owing to the unavailability of ground truths, we invited practitioners to build a gold standard serving as a benchmark to evaluate the completeness and validity of auto-generated US-AC against human-crafted ones. Additionally, we invited eight experienced agile practitioners to evaluate the quality of US-AC using the INVEST framework. The results demonstrate consistent improvements across all tested LLMs, including the LLaMA and GPT-3.5 series. Notably, SimAC significantly enhances the ability of gpt-3.5-turbo in GAC, achieving improvements of 29.48% in completeness and 15.56% in validity, along with the highest INVEST satisfaction score of 3.21/4. Furthermore, this study also provides case studies to illustrate SimAC’s effectiveness and limitations, shedding light on the potential of LLMs in automated agile requirements engineering. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.