梳理机器学习敏捷管理现状，揭示关键挑战与实践框架
Agile Management for Machine Learning: A Systematic Mapping Study
- 通过系统映射研究整合27篇论文，提炼8类核心主题
- 发现ML任务工时估算不准是普遍难题，影响项目规划
- 适合关注ML项目管理的开发者、项目经理及研究者
机器学习驱动的系统正深刻改变社会，其开发具有实验性强、数据变化快的特点，传统项目管理难以应对。敏捷方法因其灵活性和增量交付特性，看似适配此类动态环境，但如何在实际中有效应用仍不明确。本文通过混合搜索策略（数据库检索+前后追溯）开展系统映射研究，共识别2008至2024年间发表的27篇相关论文。从中提炼出8个管理框架，并将实践建议归类为8个关键主题，如迭代灵活性、创新性ML专属产物、最小可行模型等。主要发现：各研究普遍面临对ML相关任务进行准确工时估算的困难。本研究贡献在于系统呈现领域现状并指出尚存开放问题。尽管已有相关工作，但亟需更扎实的实证评估来验证其有效性。
原文摘要 · Abstract (English)
[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.
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