How to Score AI-Generated Code Before You Ship It

AI-generated code needs the same scoring as code a stranger wrote, because that is exactly what it is: code you did not write, produced fast, that you now own. Speed does not change ownership. When a model writes a feature in ten minutes, you skip the slow, careful reasoning that used to happen while a human typed it, which means the review has to catch what the writing no longer does. Here is how I score AI-generated code before it goes anywhere near production.

Treat it as code you did not write, because you did not

The dangerous instinct with model output is to trust it more than a colleague's pull request, because it reads confidently and it appeared instantly. That confidence is not evidence. The model produced something plausible, not something verified, and plausible is where the subtle bugs hide. I score it exactly like I score a repository I did not write: no benefit of the doubt, straight to the risk.

This mindset shift is the whole game. The moment you accept AI output as a draft from an unknown author rather than an answer from an authority, you start asking the right questions and stop rubber-stamping green-looking code.

Score coupling and blast radius first

AI is good at making individual functions look clean. It is much worse at fitting into the architecture around them, because it sees a narrow window of your codebase and guesses at the rest. So the first thing I check is how the new code connects to everything else.

Does it duplicate logic that already exists somewhere. Does it reach into internals it should not touch. Does it quietly widen the blast radius so that the next change now breaks in more places. Models love to solve the local problem by coupling to whatever is nearby, and that coupling is invisible in a demo and expensive forever after. This is the same structural risk I chase when auditing any repo, and it matters more with AI because volume is high and each piece looks fine alone.

Check the test signal, not the test count

AI will happily write tests for the code it just wrote, and they will pass, and that means almost nothing. The model tends to write tests that confirm the code does what the model already assumed, which is circular. The test and the code share the same blind spot.

So I do not count tests, I check whether they would catch a real regression. I break the generated code on purpose and see if its own tests notice. Often they do not, because they were written to describe the implementation rather than to constrain the behavior. This is the same coverage-lies problem I cover in code quality score vs test coverage, just amplified, because the model produces confident tests at scale.

Watch the volume problem

Here is what is genuinely new about scoring AI code. The volume. A human writes a feature a day, so review keeps pace. A model writes ten features an hour, and human review cannot keep pace, so quality quietly degrades under the flood while every individual piece looks acceptable.

This is why scoring AI-generated code has to be automated to survive its own throughput. You cannot hand-review model output at model speed. You need a system that scores every change for coupling, churn risk, and test honesty as it lands, and flags only the pieces that actually need a human. I built ReformCode partly for this reason: to put a consistent quality gate in front of code that is now being generated faster than anyone can read it. It is the natural complement to what AI-assisted development should mean, which is speed with a governor, not speed with no brakes.

Gate it, then trust it

The workflow that actually works is simple. Generate freely, because that is where the speed comes from. Then gate hard, because that is where the ownership lives. Every piece of model output passes the same scoring a stranger's code would: coupling, blast radius, real test signal, and a human read on the few pieces the score flags as high risk.

Do that and AI is a genuine multiplier, code produced fast and shipped safe. Skip the gate and AI is a machine for generating technical debt faster than you have ever generated it before, all of it looking clean on the surface. The model changed how fast the code appears. It did not change the fact that once it is in your repo, it is yours, and it will fight you on the next change exactly as hard as anything a human wrote badly.