In agile software development, breaking down user stories into actionable tasks is a critical yet time-consuming process. This paper investigates the potential of Generative AI tools to assist in task splitting, aiming to enhance planning efficiency. We conducted a controlled experiment comparing traditional task-splitting methods with AI-assisted approaches using GitLab Duo. Our findings indicate that while current AI tools are not yet mature enough to replace developers, they can aid in generating more granular task lists and ensuring no important tasks are overlooked. Participants favored a hybrid approach, combining AI tools with conventional methods to maintain high accuracy in planning. This study highlights the potential benefits and limitations of integrating Generative AI into agile development processes, suggesting that AI tools can serve as valuable aids in task splitting, provided there is human oversight to filter out irrelevant tasks.

翻译：在敏捷软件开发中，将用户故事分解为可执行的任务是一个关键但耗时的工作流程。本文探讨了生成式人工智能工具辅助任务分解的潜力，旨在提升规划效率。我们通过一项对照实验，比较了使用GitLab Duo的传统任务分解方法与AI辅助方法。研究结果表明，虽然当前AI工具尚不足以取代开发者，但它们有助于生成更细粒度的任务清单，并确保不遗漏重要任务。参与者更倾向于采用混合方法，将AI工具与传统方法相结合，以保持规划的高准确性。本研究揭示了将生成式AI融入敏捷开发流程的潜在优势与局限性，表明在人工监督以过滤无关任务的前提下，AI工具可作为任务分解过程中的有效辅助手段。