在机器学习增强系统中应用以需求为导向的敏捷管理方法
Applying a Requirements-Focused Agile Management Approach for Machine Learning-Enabled Systems
AI总结：
本文提出了一种针对机器学习增强系统的需求导向敏捷管理方法RefineML，并通过产学研合作项目验证其在提升沟通和早期可行性评估方面的有效性。
摘要：
机器学习（ML）增强系统挑战了传统的需求工程（RE）和敏捷管理，因为其依赖数据、实验和不确定的模型行为。现有的RE和敏捷实践仍然缺乏整合和不够适合这些特性。本文报告了应用RefineML的实践经验，RefineML是一种针对持续和敏捷细化ML增强系统的做法，它将ML定制的规范和敏捷管理方法与系统映射研究得出的最佳实践相结合。应用背景是圣保罗大学（PUC-Rio）与巴西网络安全公司EXA之间的产学研合作项目。为了评估目的，我们应用了评估RefineML适用性和整体接受度的问卷和半结构化访谈。我们对收集的定性数据进行了主题分析。在适用性和接受度方面，问卷的结果显示了高感知有用性和使用意图。基于访谈，利益相关者认为RefineML提高了沟通并促进了早期可行性评估，同时使ML和软件工作的双轨治理成为可能，允许在软件项目演变的同时持续细化模型。然而，仍然存在一些限制，特别是将ML问题转化为敏捷需求和估计ML工作量方面的困难。
英文摘要：
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.
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