[Paper] Automated Code Review Assignments: An Alternative Perspective of Code Ownership on GitHub

Source: arXiv - 2512.05551v1

Overview

The paper investigates how GitHub’s CODEOWNERS feature is actually used in real‑world projects and what impact it has on pull‑request (PR) review dynamics. By analyzing more than 844 k PRs across thousands of repositories, the authors show that automatically assigning reviewers can reshape ownership, speed up reviews, and improve overall project governance.

Key Contributions

• Large‑scale empirical dataset: 844 k PRs, 1.9 M comments, 2 M reviews, and 10 k identified code owners across many open‑source projects.
• Behavioral analysis of code owners: Demonstrates that code owners follow the rules in the CODEOWNERS file and exhibit collaboration patterns similar to traditional ownership metrics.
• Workflow impact: Shows that PRs involving code owners tend to progress more smoothly and close faster over time.
• Causal evidence via RDD: Uses regression discontinuity design to reveal that adopting CODEOWNERS shifts review responsibilities away from core developers toward designated owners.
• Practical guidance: Provides actionable recommendations for projects looking to strengthen security, accountability, and efficiency through automated reviewer assignment.

Methodology

1. Data collection – The authors mined GitHub’s public API to gather PRs, comments, reviews, and the contents of CODEOWNERS files from thousands of repositories.
2. Owner identification – They parsed each CODEOWNERS file to map file‑path patterns to specific GitHub usernames, yielding 10 287 distinct code owners.
3. Metric computation – For each PR they recorded whether a code owner was automatically requested, the time to first review, total review count, and comment sentiment.
4. Comparative analysis – PRs with and without code‑owner assignment were compared using descriptive statistics and survival analysis to assess speed and smoothness.
5. Causal inference – A regression discontinuity design (RDD) was applied around the point when a repository introduced a CODEOWNERS file, isolating the effect of adoption on review distribution and latency.

Results & Findings

• Rule adherence: In >85 % of cases, the reviewers automatically added by CODEOWNERS actually participated in the review, confirming that developers respect the file’s specifications.
• Collaboration similarity: Code owners’ interaction networks (e.g., co‑reviewing, commenting) resemble those of traditional owners identified via file‑change history.
• Faster PR cycles: PRs with code‑owner involvement closed on average 12 % quicker and required 8 % fewer review comments, indicating smoother negotiations.
• Ownership redistribution: After a repository adopts CODEOWNERS, the share of reviews performed by core developers drops by ~15 %, while designated owners take on a larger portion of the workload.
• Security angle: Projects that explicitly list owners for critical directories (e.g., authentication, CI scripts) see a modest reduction in post‑merge bug reports, hinting at a protective effect.

Practical Implications

• Adopt CODEOWNERS early: Teams can embed the file from the start of a project to formalize responsibility and avoid ad‑hoc reviewer selection later.
• Target high‑risk areas: By assigning owners to security‑sensitive paths, organizations can enforce mandatory review by experts, mitigating supply‑chain attack vectors.
• Balance workload: Automated assignments help distribute review duties more evenly, preventing burnout of core maintainers and fostering broader contributor engagement.
• Tooling integration: CI pipelines can query the CODEOWNERS mapping to enforce additional checks (e.g., require signed commits from owners) before merging.
• Metrics for governance: The study’s metrics (review latency, owner participation rate) can be incorporated into dashboards to monitor the health of the review process.

Limitations & Future Work

• Scope to open‑source: The analysis is limited to public GitHub repositories; private or enterprise settings may exhibit different adoption patterns.
• Owner granularity: The study treats any matching username as an owner, not accounting for team aliases or hierarchical ownership structures.
• Causal inference constraints: While RDD provides strong evidence, unobserved confounders (e.g., simultaneous process changes) could still influence results.
• Future directions: Extending the study to other platforms (GitLab, Bitbucket), exploring the impact of CODEOWNERS on security incident rates, and developing tooling to automatically suggest optimal ownership rules based on code‑change history.

Authors

• Jai Lal Lulla
• Raula Gaikovina Kula
• Christoph Treude

Paper Information

• arXiv ID: 2512.05551v1
• Categories: cs.SE
• Published: December 5, 2025
• PDF: Download PDF