AI 中文总结
该研究调查开发者在基于Gerrit的评审生态系统中如何采用关系链及影响，通过分析三个开源生态系统中多个仓库的关系链和变更，发现链流行率上升，链变更合并时间长，评审工作依链传播，提出未来相关工具应基于链推理。
AI 中文摘要
背景。开发者越来越多地通过提交相关变更序列而非整体变更来协调依赖的评审工作流程。在Gerrit中，这些依赖关系形成关系链，即把变更链接在一起的结构化评审单元。随着链变得越来越普遍，它们通过同步开销、持续集成放大和合并顺序约束来塑造评审活动。目标。我们研究开发者如何采用关系链以及这些依赖结构如何影响评审动态和结果。方法。我们分析了来自三个Gerrit生态系统（OpenStack、维基媒体和ONAP）中15个仓库的29,580个关系链，包括401,256个变更，使用曼-肯德尔趋势检验、用于链与单独变更比较的带有克利夫德尔塔的曼-惠特尼检验以及用于基础-后代依赖关系的斯皮尔曼相关性分析。结果。各项目中链的流行率从5%到49%不等，15个项目中有14个呈上升趋势。链变更合并所需时间中位数是大小匹配的单独变更的2.6倍，对于非常大的变更，差距会扩大。评审工作通过依赖链接的评审工作流程传播：基础变更评审活动与后代评审活动共变（15个项目中有14 - 15个项目的rho = 0.43 - 0.61），并且33.5%的链成员在评审期间经历结构演变。结论。关系链作为持久的、受生态系统影响的协调单元运行，其内部结构是基于变更的分析无法捕捉的。未来的评审分析、评审员分配系统和人工智能辅助评审工具应基于链而非孤立的变更进行推理。
英文摘要
Background. Developers increasingly coordinate dependent review workflows by submitting sequences of related changes rather than monolithic ones. In Gerrit, these dependencies form relation chains: structured review units that link changes together. As chains become more common, they shape review activities through synchronization overhead, CI amplification, and merge-ordering constraints. Aim. We investigate how developers adopt relation chains and how these dependency structures influence review dynamics and outcomes. Method. We analyze 29,580 relation chains from 15 repositories across three Gerrit ecosystems (OpenStack, Wikimedia, and ONAP), comprising 401,256 changes, using Mann-Kendall trend tests, Mann-Whitney tests with Cliff's delta for chain-vs-solo comparisons, and Spearman correlations for base-descendant dependencies. Results. Chain prevalence ranges from 5% to 49% across projects, increasing in 14 of 15. Chain changes take a median of 2.6 times longer to merge than size-matched solo changes, with the gap widening for very large changes. Review effort propagates through dependency-linked review workflows: base-change review activity co-varies with descendant review activity (rho = 0.43-0.61 in 14-15 of 15 projects), and 33.5% of chain members undergo structural evolution during review. Conclusions. Relation chains operate as durable, ecosystem-shaped coordination units with internal structure that change-centric analyses cannot capture. Future review analytics, reviewer-assignment systems, and AI-assisted review tools should reason over chains rather than isolated changes.
