AI 中文总结
研究三个深度学习开源社区治理实践，用制度分析与发展框架分析文档与提交记录，识别规则主题、治理功能，发现操作与结构规则特点不同，最终为成熟大规模OSS项目得出33条可操作治理实践。
AI 中文摘要
开源软件（OSS）项目中的协作在不同贡献者群体间带来了重大的协调和质量控制挑战。项目通过成文的治理规则应对这些挑战，但维护者在编纂哪些规则、何时引入或修订规则以及如何在文档中组织规则方面缺乏系统指导。我们对三个成熟的深度学习框架PyTorch、TensorFlow和Paddle进行了混合方法实证研究。使用制度分析与发展框架，我们分析了109份文档并识别出七种规则类型中的17个规则主题。操作规则如工作流程在所有三个项目中都有出现，而结构规则如角色层级差异更大。追踪1700多次提交，我们发现操作规则主题通常出现更早且修订更频繁，许多结构规则主题出现较晚且变化较少。含规则的内容在特定任务和角色的文件中也变得越来越专业化。我们还识别出实质性规则变化中体现的四个治理功能：规范对齐、工作流程优化、协调结构化以及社区与治理发展。综合这些发现，我们为有大量协调需求和组织参与的成熟大规模OSS项目得出33条可操作的治理实践。
英文摘要
Collaboration in Open Source Software (OSS) projects creates substantial coordination and quality-control challenges across diverse contributor bases. Projects address these challenges through documented governance rules, yet maintainers have limited systematic guidance on what rules to codify, when to introduce or revise them, and how to organize them across documents. We conducted a mixed-methods empirical study of three mature deep learning frameworks: PyTorch, TensorFlow, and Paddle. Using the Institutional Analysis and Development framework, we analyzed 109 documents and identified 17 rule themes across seven rule types. Operational rules, such as workflows, appeared across all three projects, whereas structural rules, such as role hierarchies, varied more substantially. Tracing more than 1,700 commits, we found that operational rule themes generally appeared earlier and were revised more frequently, while many structural rule themes emerged later and changed less often. Rule-bearing content also became increasingly specialized across task- and role-specific files. We further identified four governance functions reflected in substantive rule changes: Norm Alignment, Workflow Refinement, Coordination Structuring, and Community and Governance Development. Synthesizing these findings, we derive 33 actionable governance practices for mature, large-scale OSS projects with substantial coordination demands and organizational involvement.
