Machine Learning (ML)-enabled systems challenge traditional Requirements Engineering (RE) and agile management due to data dependence, experimentation, and uncertain model behavior. Existing RE and agile practices remain poorly integrated and insufficiently tailored to these characteristics. This paper reports on the practical experience of applying RefineML, a requirements-focused approach for the continuous and agile refinement of ML-enabled systems, which integrates ML-tailored specification and agile management approaches with best practices derived from a systematic mapping study. The application context concerns an industry-academia collaboration project between PUC-Rio and EXA, a Brazilian cybersecurity company. For evaluation purposes, we applied questionnaires assessing RefineML's suitability and overall acceptance and semi-structured interviews. We applied thematic analysis to the collected qualitative data. Regarding suitability and acceptance, the results of the questionnaires indicated high perceived usefulness and intention to use. Based on the interviews, stakeholders perceived RefineML as improving communication and facilitating early feasibility assessments, as well as enabling dual-track governance of ML and software work, allowing continuous refinement of the model while evolving the overall software project. However, some limitations remain, particularly related to difficulties in operationalizing ML concerns into agile requirements and in estimating ML effort.

翻译：机器学习（ML）赋能系统因其数据依赖性、实验性质及模型行为的不确定性，对传统需求工程（RE）与敏捷管理提出了挑战。现有的RE与敏捷实践仍存在整合不足且未能充分适应这些特性的问题。本文报告了应用RefineML的实践经验，这是一种面向需求的、用于持续敏捷精化ML赋能系统的方法，它整合了针对ML定制的规范与敏捷管理方法，并融入了系统映射研究中得出的最佳实践。应用背景涉及巴西天主教大学与巴西网络安全公司EXA之间的产学研合作项目。为进行评估，我们采用了评估RefineML适用性与整体接受度的问卷以及半结构化访谈，并对收集的定性数据进行了主题分析。在适用性与接受度方面，问卷结果显示其感知有用性和使用意愿均较高。基于访谈，利益相关者认为RefineML改善了沟通、促进了早期可行性评估，并实现了ML工作与软件工作的双轨治理，从而能够在推进整体软件项目的同时持续精化模型。然而，该方法仍存在一些局限性，特别是将ML关注点操作化为敏捷需求以及估算ML工作量方面存在困难。