How Developers Use Relation Chains in Gerrit-Based Review Ecosystems: An Empirical Study Across Three Open-Source Ecosystems

2026-07-22

AI Summary

This study addresses the unclear usage patterns of change dependency chains in Gerrit code review and their impact on review dynamics. Analyzing nearly 30,000 dependency chains and over 400,000 changes across three major open-source ecosystems—OpenStack, Wikimedia, and ONAP—it reveals that dependency chains function as structural units for cross-project coordination. Employing Mann-Kendall trend tests, Mann-Whitney U tests (with Cliff’s delta effect sizes), and Spearman correlations, the research finds that such chains constitute 5%–49% of all changes and are increasing over time. Changes within chains take 2.6 times longer to merge than standalone changes, 33.5% of chains undergo structural evolution during review, and review activity on descendant changes correlates positively with that on the base change (ρ = 0.43–0.61). The findings advocate analyzing changes at the chain level rather than in isolation to improve review efficiency.

Abstract

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.

Problem

• relation chains
• code review
• Gerrit
• dependency structures
• review dynamics

Innovation

• relation chains
• code review
• dependency analysis
• empirical study
• Gerrit