How to Test AI-Generated Code

A year ago, the slow step in shipping a feature was writing it. Now, for a lot of teams, the slow step is being sure it works. AI coding assistants produce plausible, syntactically clean code at a rate that outpaces careful review — and testing, which was already the part teams under-invested in, is the thing that falls behind first.

What "testing AI-generated code" actually means

Testing AI-generated code is the practice of verifying code produced by an assistant against the same bar you'd hold hand-written code to — correct behavior, safe edge cases, no regressions — while accounting for the specific ways generated code fails: confident-looking mistakes, subtle misreadings of intent, and missing cases the model never considered. The goal isn't to distrust the tool; it's to close the gap between how fast code arrives and how fast it's validated.

The failure modes are different enough from human error to be worth naming:

• Plausible-but-wrong. Generated code compiles, reads well, and does almost the right thing — off-by-one in a boundary, the wrong comparison operator, a currency rounded at the wrong step. Human reviewers skim clean-looking code; these slip through.
• Intent drift. The model solved a slightly different problem than the one you had in mind — handling the happy path you described while quietly ignoring the constraint you mentioned once, three messages ago.
• Missing cases. Empty inputs, concurrent access, the logged-out state, the expired token. A model generates what's typical, and typical code doesn't handle the untypical.
• Silent integration breaks. The function is fine in isolation but changes an assumption three callers upstream depended on.

Where to put your effort

You can't review every generated line as carefully as you'd write it — that would erase the speed-up. Concentrate testing where the risk actually is.

1. Behavior at the boundaries, not the center

The happy path is the part the model is best at. Spend your test budget on the edges: empty and maximal inputs, permission boundaries, error paths, states that only exist briefly (mid-migration, partially-filled forms, a session that expires mid-flow). Write the assertions the prompt didn't mention.

2. The seams between generated and existing code

A generated function dropped into an existing codebase is only as safe as its interface with everything around it. Test the callers. Test what happens when the new code returns an error the old code didn't expect. Integration-level tests catch this class of problem that unit tests on the new function never will — see unit vs. integration vs. end-to-end for where each fits.

3. End-to-end flows, because that's what users touch

Generated code often changes UI behavior in ways that look right in a diff and wrong in a browser: a button that no longer wires to its handler, a form that submits twice, a redirect that lands somewhere unexpected. The only reliable check is to actually drive the flow the way a user would.

A practical workflow

1. Make the assistant write tests too — then don't trust them blindly. Generated tests are a useful starting point, but they tend to assert what the code does, not what it should do. Read them, and add the cases they're missing (especially the failure cases).
2. Run the full suite on every AI-assisted change, not just at PR time. The faster code arrives, the more often "just a small change" turns out not to be. Cheap, frequent test runs are what keep intent drift from compounding.
3. Diff behavior, not just code. For anything user-facing, compare how the app behaves before and after the change — not only what lines moved. A clean-looking diff can still change what happens on screen.
4. Keep a regression net that grows with the code. Every bug a generated change introduces becomes a test, so the same class of mistake can't come back the next time the model touches that area.
5. Re-check coverage after the fact. Generated code expands your app faster than your test suite. Periodically look at what's actually being exercised and where new, untested surface has appeared.

The coverage problem is structural

The core issue isn't any single generated function — it's the rate. Scripted end-to-end tests only cover paths someone thought to script, and nobody is scripting fast enough to keep pace with an assistant that adds three flows in an afternoon. That's the gap manual QA and selector-based test suites can't close on a fast release cycle.

One way to keep the net wide enough is autonomous exploration: instead of scripting fixed paths, an agent explores the app on every run, builds a model of what it does, and flags where behavior broke — including in flows that only exist because an assistant added them last week. It won't replace targeted assertions on your critical business rules, but it covers the breadth that scripted tests structurally can't. That's the approach behind Manta: point it at your app, and it re-explores and re-checks the whole thing each run, no test code to keep in sync.

The takeaway

Testing AI-generated code isn't a new discipline — it's the discipline you already had, under more pressure. Hold generated code to the same bar, aim your effort at the boundaries and the seams, verify behavior end-to-end, and make your coverage grow at the rate your codebase now grows. The teams that stay fast are the ones whose testing sped up too.

Want broad coverage that keeps pace with AI-assisted development? Start a free run — point Manta at your app and see what it finds.