用大模型评估敏捷史诗文档质量，提升需求清晰度
A Case Study Investigating the Role of Generative AI in Quality Evaluations of Epics in Agile Software Development
- 用大语言模型自动分析敏捷史诗的完整性与清晰度
- 17位产品经理反馈满意度高，认为能有效改进需求文档
- 适合产品管理、AI辅助开发团队参考
生成式AI的普及为敏捷软件开发等场景带来新机遇。敏捷史诗是产品经理向利益相关方传达需求的关键文档，但实践中常定义不清，导致返工、交付延迟和成本超支。本行业案例研究探讨大型语言模型（LLMs）在一家全球公司中评估敏捷史诗质量的应用潜力。对17名产品经理的用户研究表明，LLM评估可融入其工作流程，具有显著感知价值，并有助于改进史诗文档。高满意度表明敏捷史诗是AI评估的新可行应用场景。然而，研究也揭示了实际集成中的挑战、局限与采纳障碍，可为从业者和研究者提供重要参考。
原文摘要 · Abstract (English)
The broad availability of generative AI offers new opportunities to support various work domains, including agile software development. Agile epics are a key artifact for product managers to communicate requirements to stakeholders. However, in practice, they are often poorly defined, leading to churn, delivery delays, and cost overruns. In this industry case study, we investigate opportunities for large language models (LLMs) to evaluate agile epic quality in a global company. Results from a user study with 17 product managers indicate how LLM evaluations could be integrated into their work practices, including perceived values and usage in improving their epics. High levels of satisfaction indicate that agile epics are a new, viable application of AI evaluations. However, our findings also outline challenges, limitations, and adoption barriers that can inform both practitioners and researchers on the integration of such evaluations into future agile work practices.
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