AI code review tools vs. verification: what each one catches
AI code review tools read a pull request and comment on likely problems. Verification tools check the change against an independent record of required behavior and return pass or block evidence. Review finds issues in the diff; verification proves the product still keeps its promises. AI-generated code needs both, plus linters, security scanning, and tests that are themselves tested.
The core difference: where the source of truth lives
Every quality tool compares the code to something. The useful question is what, and whether the AI that wrote the change could have changed it too.
- Linters and type checkers compare code to language and style rules.
- Security scanners compare code to known vulnerability patterns.
- AI review bots compare the diff to general programming knowledge and the surrounding code.
- Tests compare behavior to the expectations written in the test files, which an agent can edit in the same change.
- Independent verification compares behavior to a reviewed record of requirements that the agent cannot approve.
Only the last kind can tell an intended rule change from an accidental one, because only the last kind knows what the rule was supposed to be.
What each layer catches and misses
| Layer | Examples | Catches | Misses |
|---|---|---|---|
| Linters and type checking | ESLint, TypeScript | Type errors, unsafe patterns, unused code, some common bugs | Logic errors and business rules |
| Static security analysis | CodeQL, Semgrep, Snyk Code | Injection, XSS, hard-coded secrets, known vulnerable patterns | App-specific authorization logic and business rules |
| AI code review bots | CodeRabbit, GitHub Copilot code review | Likely bugs, missing error handling, readability, diff summaries | Whether a behavior change was intended; shares the diff’s blind spots |
| Code quality platforms | SonarQube | Maintainability, duplication, coverage thresholds, quality gates | Whether tests assert the right behavior |
| Automated tests | Vitest, Jest, Playwright | Regressions in the behavior the tests encode | Anything, if the tests were rewritten in the same change |
| Mutation testing | StrykerJS | Tests that run code without checking its results | Requirements that have no test at all |
| Independent verification | MaruCheck | Changes to approved behavior even when tests were updated to match, repeat regressions, weak tests on changed code | Style and generic vulnerabilities; needs requirements written down first |
Why AI review alone is not enough for AI-written code
AI review bots are good at what human reviewers find tedious: spotting a missing null check, an unhandled promise, or a confusing name across a large diff. Use them.
But an AI reviewer and an AI coding agent are reading the same evidence. If the agent changed a free-plan limit from 5 to 10 and updated the test to match, the diff is internally consistent. The code is clean, the test passes, and nothing in the pull request says the old limit was a business decision. A reviewer, human or AI, that only sees the diff has no reason to object. Catching it requires a record of the approved limit that lives outside the change.
Which layers you need
| Situation | Start with |
|---|---|
| Solo developer or vibe-coded app | Type checking, end-to-end tests for your key promises, and a CI job that runs them. See testing a vibe-coded app. |
| Small team using coding agents daily | All of the above, plus an AI review bot as a first pass, protected test files, and independent verification on critical paths. |
| Billing, health, finance, or regulated data | Every layer, with mutation checks on critical code and a recorded human approval for any change to required behavior. |
Four questions to ask any AI code quality tool
- What does it compare the code to? If the answer is only the diff, it cannot detect an intended-looking but unapproved change.
- Can the coding agent change that reference? If the agent can edit the tests or the config the tool relies on, the check is not independent.
- Does it produce evidence or opinions? A comment saying a change “looks fine” is not the same as a reproducible test result tied to a requirement.
- Where does it run, and what leaves your network? Some tools upload source code to a hosted service; others run entirely in your own CI.
Frequently asked questions
- What is the difference between AI code review and code verification?
- AI code review tools read a pull request and comment on likely problems, the way a reviewer would. Verification tools check the change against a separate, approved record of required behavior and produce pass or block evidence. Review finds issues in the diff; verification proves the product still keeps its promises.
- What tools should I use to check AI-generated code?
- No single tool covers it. A practical stack combines a linter and type checker, a static security scanner, an AI or human reviewer for the diff, targeted tests with mutation checks, and an independent behavior check that the coding agent cannot edit. Each layer catches failures the others miss.
- Is MaruCheck an AI code review tool?
- MaruCheck is not an AI code reviewer. It does not comment on style or suggest edits. It is an open-source verification CLI that checks each Git diff against reviewed Quality Contracts, runs requirement-linked tests and mutation checks, and returns a ship-or-block decision with reproducible evidence. It complements review bots rather than replacing them.
Related guides
How to verify AI-generated code before it ships
A tool-agnostic process for verifying code from Claude Code, Cursor, Copilot, or Codex: keep the spec outside the tests, target risk, and test the tests.
GUIDEHow to review an AI-generated pull request
A reviewer's checklist for AI-written pull requests: what to read first, failure patterns that look correct, and what evidence to require before approving.
GUIDEMutation testing in TypeScript and JavaScript
What mutation testing is, how to read a mutation score, running StrykerJS with Jest or Vitest, and mutation-testing only the code an AI just changed.
GUIDEWhy AI coding agents change tests to make them pass
Why Claude Code, Cursor, and other coding agents edit, weaken, or skip tests to get a green run, how to spot it in a diff, and five controls that stop it.
Last updated