# 3100 Opinions on Code Review in an AI World: Building Causal Theory from Practitioner Discourse arXiv: https://arxiv.org/abs/2607.07980 ยท HTML: https://mlsanity.com/paper/2607.07980/ ## Key facts - No citing papers in the map yet (data through 14 Oct 2026). - Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. - Builds on 23 of its 95 references that are in the map. - First posted on arXiv 8 Jul 2026. ## Placed in - LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/) ## Builds on (most cited first, 23 of 23) - [Guidelines for Empirical Studies in Software Engineering involving Large Language Models](https://mlsanity.com/paper/2508.15503/) (2025) - [Where Do AI Coding Agents Fail? An Empirical Study of Failed Agentic Pull Requests in GitHub](https://mlsanity.com/paper/2601.15195/) (2026) - [Debt Behind the AI Boom: A Large-Scale Empirical Study of AI-Generated Code in the Wild](https://mlsanity.com/paper/2603.28592/) (2026) - [The Rise of AI Teammates in Software Engineering (SE) 3.0: How Autonomous Coding Agents Are Reshaping Software Engineering](https://mlsanity.com/paper/2507.15003/) (2025) - [More Code, Less Reuse: Investigating Code Quality and Reviewer Sentiment towards AI-generated Pull Requests](https://mlsanity.com/paper/2601.21276/) (2026) - [On Autopilot? An Empirical Study of Human-AI Teaming and Review Practices in Open Source](https://mlsanity.com/paper/2601.13754/) (2026) - [Understanding Dominant Themes in Reviewing Agentic AI-authored Code](https://mlsanity.com/paper/2601.19287/) (2026) - [Investigating Autonomous Agent Contributions in the Wild: Activity Patterns and Code Change over Time](https://mlsanity.com/paper/2604.00917/) (2026) - [Why Are Agentic Pull Requests Merged or Rejected? An Empirical Study](https://mlsanity.com/paper/2605.22534/) (2026) - [Why Agentic-PRs Get Rejected: A Comparative Study of Coding Agents](https://mlsanity.com/paper/2602.04226/) (2026) - [From Industry Claims to Empirical Reality: An Empirical Study of Code Review Agents in Pull Requests](https://mlsanity.com/paper/2604.03196/) (2026) - [Impact of LLM-based Review Comment Generation in Practice: A Mixed Open-/Closed-source User Study](https://mlsanity.com/paper/2411.07091/) (2024) - [Code Change Characteristics and Description Alignment: A Comparative Study of Agentic versus Human Pull Requests](https://mlsanity.com/paper/2601.17627/) (2026) - [Beyond Bug Fixes: An Empirical Investigation of Post-Merge Code Quality Issues in Agent-Generated Pull Requests](https://mlsanity.com/paper/2601.20109/) (2026) - [When AI Teammates Meet Code Review: Collaboration Signals Shaping the Integration of Agent-Authored Pull Requests](https://mlsanity.com/paper/2602.19441/) (2026) - [A Task-Level Evaluation of AI Agents in Open-Source Projects](https://mlsanity.com/paper/2602.02345/) (2026) - [A Large-Scale Comprehensive Measurement of AI-Generated Code in Real-World Repositories](https://mlsanity.com/paper/2603.27130/) (2026) - [On the Footprints of Reviewer Bots Feedback on Agentic Pull Requests in OSS GitHub Repositories](https://mlsanity.com/paper/2604.24450/) (2026) - [These Aren't the Reviews You're Looking For How Humans Review AI-Generated Pull Requests](https://mlsanity.com/paper/2605.02273/) (2026) - [Habituation at the Gate: Rising Approval and Declining Scrutiny in Human Review of AI Agent Code](https://mlsanity.com/paper/2606.22721/) (2026) - [How does Software Change?](https://mlsanity.com/paper/2106.01885/) (2021) - [The Quiet Contributions: Insights into AI-Generated Silent Pull Requests](https://mlsanity.com/paper/2601.21102/) (2026) - [A Grounded Theory of Debugging in Professional Software Engineering Practice](https://mlsanity.com/paper/2602.11435/) (2026) ## Abstract (arXiv) Coding agents now author entire pull requests, and practitioners sharply disagree about what this does to code review: whether it becomes the bottleneck, whether human review is still necessary, and whether it quietly erodes the understanding that it once built. Repository-mining studies measure surface trends but seldom explain the mechanisms beneath them, and the trends themselves prove unstable. A motivating observational analysis of public GITHUB activity finds that agent-authored pull requests are reviewed less often, merged several times faster, and discussed less than human-authored ones, yet the direction of these trends flips under different but equally defensible analysis choices, so the traces establish what is changing without explaining why. To recover the mechanisms, we synthesize practitioner discourse at scale into an explanatory theory: we collect 38,709 grey-literature documents (engineering blogs and Reddit threads), filter to those substantively about code review, and code a stratified random sample of 3,100 with an LLM-assisted pipeline, from which we build a causal model of 26 constructs and 67 relationships (64 directed, 3 contested). Its organizing claim is that review is the control point through which a coding agent's effect on software is decided, and that AI does not fix the sign of that effect: the team sets it, through the expertise its humans bring and how it structures the review process. The theory makes the competing positions explicit and turns "AI is changing code review" into falsifiable propositions with named constructs and moderators. As a secondary contribution, we offer the underlying LLM-assisted, grey-literature theory-building method as a scalable template for software-engineering research, with a public implementation.