Since February 27, 2026, every pull request in our active repositories has been reviewed by CodeRaven, the AI code-review platform we built and operate. That gives us 148 days of production history. It isn’t a benchmark or a vendor demo. It’s the complete record of what an AI reviewer actually did across our own and our clients’ codebases, including the parts that flatter nobody.
Most claims about AI code review come from the companies selling it, measured on curated examples. We sell it too (CodeRaven is our product), so read this with that in mind. What we can offer is the full dataset, published with its limitations attached. And honestly, the most interesting findings aren’t the ones we would have picked.
The headline numbers
- 1,164 pull requests reviewed over 148 days
- 2,812 reviews, about 2.4 per PR, since PRs get re-reviewed as they change
- 14,552 findings posted as review comments
- ~7 million lines touched (+5,477,927 added / −1,478,062 removed)
- 76.0% of AI Code Review approved; 24.0% requested changes
- Severity distribution across reviews: 1,441 low · 1,134 medium · 237 high · 0 critical
- Total AI spend: $161.20
One in four PRs was asked to change
If you want one number that answers whether AI Code Review actually pushes back or just rubber-stamps, it’s the 24% changes-requested rate. One PR in four got stopped and revised. That rate wasn’t constant. It started much rougher, 57.9% approval in the first partial week, and settled into a band between 72% and 81% as the codebases absorbed feedback and authors got used to being reviewed.
237 high-severity catches, zero critical ones
Over five months, 237 reviews flagged high-severity issues. The critical count is zero, and we’re reporting that as-is rather than dressing it up: either nothing catastrophic entered a pull request in 148 days, or things that would have become catastrophic were caught while they were still merely “high.” The data can’t distinguish those two stories, and neither can we.
What the number does tell you is that real problems worth blocking a merge showed up at a rate of about 1.6 per day, every day, across three active repositories. Code review, human or AI, exists for that steady drip, not for the dramatic save.
The AI Code Review cost: $161.20. Total.
Not per month. The entire 148-day, 14,552-finding history cost $161.20 in AI spend. That works out to roughly 1.1 cents per finding and about 68 cents per high-severity catch.
For comparison, price out fifteen minutes of a senior engineer’s time reading a diff, spotting the issue, and writing it up. This isn’t “cheaper than a human reviewer.” It’s a different category of expense. The whole AI Code Review layer costs less per month than the coffee at the standup where you’d debate turning it off. At that price, the question stops being whether AI review is worth it and becomes what the argument is for not running it.
Month by month: volume nearly doubled, scrutiny held
| Month (2026) | PRs | Reviews | Low / Med / High | Approval |
|---|---|---|---|---|
| Feb (27–28) | 16 | 19 | 10 / 8 / 1 | 57.9% |
| Mar | 200 | 420 | 269 / 123 / 28 | 75.5% |
| Apr | 227 | 431 | 244 / 160 / 27 | 81.2% |
| May | 194 | 559 | 258 / 240 / 61 | 76.9% |
| Jun | 283 | 775 | 385 / 316 / 74 | 72.3% |
| Jul (1–24) | 244 | 608 | 275 / 287 / 46 | 77.3% |
Review volume nearly doubled from March (420) to June (775) as re-review activity grew: more iterations per PR, not just more PRs. The approval rate stayed in a narrow band while that happened, which is the pattern you’d want. Throughput scaled without the reviewer getting more lenient. May and June also carried the heaviest high-severity load (61 and 74), the same months with the deepest re-review cycles.
The clearest pattern in the author data: churn predicts pushback
CodeRaven reviews PRs from eight contributors. Three data points describe the spread:
- Our highest-volume contributor by PR count: 364 PRs, 83.3% approval
- Highest approval rate in the dataset: 84.8%
- Our highest-churn contributor: 196 PRs, 2.63 million lines added, and the lowest approval rate: 62.3%
The contrast worth noticing: PR count alone doesn’t predict pushback. The contributor with the most PRs holds an 83.3% approval rate, close to the best in the dataset. The contributor who moved the most code, 2.63 million lines across 196 PRs, sits at the bottom. GitClear has been documenting this churn-quality correlation industry-wide as AI-assisted coding pushes churn and duplication up. We can see it reproduced at the level of individual contributors in our own repos. The more code you move per unit time, the more often review says stop.
The honest reading is that approval rate mostly measures blast radius per change. Small, contained changes sail through AI Code Review; big sweeping ones get stopped. When AI helps you generate more code faster, independent review gets more necessary, not less. The throughput is real, and so is the debt.
What this dataset can’t tell you about AI Code Review
Every “state of” report should have this section; here’s ours.
- Severity is the only structured category. CodeRaven doesn’t currently tag findings by issue type, so we can’t rank “most common mistake” from structured data alone. A text-mining pass over the 14,552 review comments is the planned follow-up to this report.
- Three active repositories. Eight repos are monitored, but the volume comes from three internal and client repositories. This is one team’s production reality, not an industry sample.
- Month boundaries are approximate. Monthly rows come from rolling-window differencing and can be off by one or two at the edges. All-time totals are exact.
- We built the reviewer. We’ve published the raw counts so you can discount our interpretation and keep the data.
What we’d do with these numbers
If you’re running AI-assisted development (Copilot, Claude, Cursor, agents), here are the takeaways we act on ourselves:
- Put review where the churn is. Our data says high churn predicts changes-requested. If AI is multiplying your line count, that’s precisely where an independent reviewer belongs.
- Treat sub-penny review as default-on. At 1.1 cents per finding, the cost argument against a second set of eyes no longer exists.
- Watch approval-rate drift, not just bug counts. A reviewer that gets more lenient as volume grows is decorative. Ours held between 72% and 81% while volume nearly doubled, and that band is the health metric we track.
If you have a codebase that grew faster than its foundations, AI-generated or otherwise, that’s what our AI code rescue practice is for, with CodeRaven doing the systematic first pass. If you’re building LLM systems and want evaluation and AI Code Review baked in from the start, that’s custom LLM development. And if you just want the reviewer, CodeRaven is the product all of this data came from.
