Recoleta
GitHub
Source note

Rethinking Code Review in the Age of AI: A Vision for Agentic Code Review

Authors
Hüseyin Özgür Kamalı, Erdem Tuna, Vahid Haratian, Eray Tüzün
Source
arXiv
Published
Collection
Software Intelligence
Code ReviewAI AgentsSoftware EngineeringHuman AI CollaborationPull Requests
Open arXivMarkdown source

Summary

The paper argues that AI-assisted coding increases review load, so code review needs an agentic, human-controlled PR workflow rather than isolated automation tools. It proposes a five-stage process for carrying context across PR creation, augmentation, reviewer selection, review, and retrospective.

Problem

  • AI coding assistants can raise code production speed by more than 50%, which increases the amount of code that must pass through PR review.
  • Current AI review support is split across narrow tasks such as reviewer recommendation, PR description generation, and comment suggestion, so context is lost between stages.
  • Reviewers still face missing rationale, poor reviewer assignment, uneven feedback quality, privacy risks, bias, automation bias, and weak evaluation methods.

Approach

  • The core mechanism is a coordinated PR workflow with specialized agents assigned to different review tasks, while humans keep final control at key quality gates.
  • The proposed workflow has five stages: PR Creation, PR Augmentation, Reviewer Selection, AI-Assisted Code Review, and PR Retrospective.
  • Agents enrich PRs with rationale, issue links, summaries, risk signals, reviewer suggestions, and review support so later stages reuse earlier context.
  • Human reviewers remain responsible for judgment, accountability, team knowledge, and merge decisions.
  • The paper also gives a research agenda for reliability, transparency, privacy, bias, evaluation metrics, and human-AI authority boundaries.

Results

  • The paper reports no new benchmark, prototype evaluation, user study, or controlled experiment for the proposed agentic review workflow.
  • Its main concrete claim is a five-stage agentic PR process designed to connect tasks that current tools handle separately.
  • The motivation cites prior evidence that AI coding assistants accelerate individual coding tasks by more than 50%, while AI-generated contributions require more review iterations than human-written ones.
  • The background cites Fagan inspections detecting up to 80% of errors before testing in some cases.
  • The background cites Google resolving more than 1,000 issues flagged by FindBugs in review, 75% of GitHub projects mandating peer review for contributions, and over 90% of analyzed GitHub projects using CI services.
  • The background cites Microsoft branch studies where long-lived branches delayed integration by nearly 9 additional days on average.

Link