[2605.30208] Automating Low-Risk Code Review at Meta: RADAR, Risk Calibration, and Review Efficiency

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arXiv:2605.30208 (cs)

[Submitted on 28 May 2026 (v1), last revised 12 Jun 2026 (this version, v2)]

Title:Automating Low-Risk Code Review at Meta: RADAR, Risk Calibration, and Review Efficiency

Authors:Chris Adams, Arjun Singh Banga, Parveen Bansal, Souvik Bhattacharya, Payal Bhuptani, Rujin Cao, Pedro Canahuati, Nate Cook, Brian Ellis, Prabhakar Goyal, Gurinder Grewal, Tianyu He, Matt Labunka, Alex Manners, David Molnar, Ging Cee Ng, Vishal Parekh, Jiefu Pei, Frederic Sagnes, James Saindon, Will Shackleton, Sid Sidhu, Gursharan Singh, Karthik Chengayan Sridhar, Matt Steiner, Pratibha Udmalpet, Sean Xia, Stacey Yan, Audris Mockus, Peter Rigby, Nachiappan Nagappan<br>View a PDF of the paper titled Automating Low-Risk Code Review at Meta: RADAR, Risk Calibration, and Review Efficiency, by Chris Adams and 30 other authors

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Abstract:AI-assisted coding tools have altered software production. At Meta, significant lines of code per human-landed diff grew by 105.9% year over year and per-developer diff volume rose 51%, with agentic AI responsible for over 80% of that growth. Meanwhile, the share of diffs receiving timely review has declined, exposing a widening gap between code supply and reviewer bandwidth. We ask three questions that progress from feasibility through calibration to impact: (1) can risk-stratified automation operate at scale across diverse organizations, (2) how does tuning the risk threshold affect the trade-off between automation yield and safety, and (3) to what extent does automated review reduce end-to-end latency for AI-generated changes? We deployed RADAR (Risk Aware Diff Auto Review), a multi-stage funnel that classifies each diff by authorship and source type, applies eligibility gates, static heuristics, a machine-learned Diff Risk Score, LLM-based Automated Code Review, and deterministic validation before landing qualifying changes. We evaluate RADAR through telemetry covering 535K+ RADAR-reviewed diffs, observational before-after comparisons for policy changes, and difference-in-differences analysis of efficiency outcomes. RADAR has reviewed 535K+ diffs and landed 331K+. Relaxing the Diff Risk Score threshold from the 25th to the 50th percentile increased the approve rate to 60.31%. The revert rate for RADAR-reviewed diffs is 1/3 that of non-RADAR diffs, and the Production Incident rate is 1/50 that of non-RADAR diffs. RADAR reduces median time to close by over 330% and median diff review wall time by 35%. Risk-aware layered automation can materially reduce review bottlenecks created by AI-driven code growth without compromising production safety.

Subjects:

Software Engineering (cs.SE); Artificial Intelligence (cs.AI)

Cite as:<br>arXiv:2605.30208 [cs.SE]

(or<br>arXiv:2605.30208v2 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2605.30208

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arXiv-issued DOI via DataCite

Submission history<br>From: Audris Mockus [view email]<br>[v1]<br>Thu, 28 May 2026 16:44:07 UTC (196 KB)

[v2]<br>Fri, 12 Jun 2026 22:21:34 UTC (196 KB)

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