标题： 敏捷机器学习管理：系统映射研究 显示英文标题

标题： Agile Management for Machine Learning: A Systematic Mapping Study

摘要： [背景] 机器学习（ML）使能的系统存在于我们的社会中，推动了重大的数字化转型。 ML开发的动态特性，以实验周期和数据的快速变化为特点，给传统项目管理带来了挑战。 敏捷方法以其灵活性和增量交付，似乎非常适合应对这种动态性。 然而，尚不清楚如何在ML使能的系统背景下有效应用这些方法，因为这些挑战需要定制化的解决方案。 [目标] 我们的目标是概述ML使能系统敏捷管理的最新进展。 [方法] 我们使用混合搜索策略进行系统映射研究，该策略结合了数据库搜索与向后和向前的雪球迭代。 [结果] 我们的研究识别出2008年至2024年间发表的27篇论文。 从中，我们识别出八个框架，并将建议和实践归类为八个关键主题，例如迭代灵活性、创新的ML特定工件和最小可行模型。 各研究中识别出的主要挑战是准确估算ML相关任务的工作量。 [结论] 本研究通过映射最新进展并识别该领域的开放差距做出了贡献。 虽然存在相关工作，但仍需要更稳健的实证评估来验证这些贡献。 显示英文摘要

摘要： [Context] Machine learning (ML)-enabled systems are present in our society, driving significant digital transformations. The dynamic nature of ML development, characterized by experimental cycles and rapid changes in data, poses challenges to traditional project management. Agile methods, with their flexibility and incremental delivery, seem well-suited to address this dynamism. However, it is unclear how to effectively apply these methods in the context of ML-enabled systems, where challenges require tailored approaches. [Goal] Our goal is to outline the state of the art in agile management for ML-enabled systems. [Method] We conducted a systematic mapping study using a hybrid search strategy that combines database searches with backward and forward snowballing iterations. [Results] Our study identified 27 papers published between 2008 and 2024. From these, we identified eight frameworks and categorized recommendations and practices into eight key themes, such as Iteration Flexibility, Innovative ML-specific Artifacts, and the Minimal Viable Model. The main challenge identified across studies was accurate effort estimation for ML-related tasks. [Conclusion] This study contributes by mapping the state of the art and identifying open gaps in the field. While relevant work exists, more robust empirical evaluation is still needed to validate these contributions.