Keep the AI coding speed, lose the bugs
AI coding tools speed up delivery, and they also produce plausible code with missing checks. We add the gates that catch it without slowing the team down.
The whole story, short
The problem
Generated code looks right but misses access checks and edge cases.
What we did
- Pair test generation with human review
- Scan AI-written code for security issues
- Add coverage gates
- Build regression suites for fast-changing code
- Test authorization and edge cases
The outcome
Changed-line coverage and security gates
What goes wrong
- Generated code looks right but misses access checks and edge cases.
- AI-written tests pass without actually catching defects.
- Security issues and secrets slip into pull requests.
- Defects rise as more code is written by tools.
Why the usual fix falls short
Code review alone cannot keep up with AI-assisted volume, and coverage numbers say nothing about whether tests would catch a real bug.
How we tested it
Pair test generation with human review
Draft tests with AI tools, have engineers check them against requirements and use mutation testing to see which tests catch defects.
Scan AI-written code for security issues
Add static analysis and secret scanning to every pull request, then triage existing findings by severity.
Add coverage gates
Require coverage on changed lines, which stops the decline without retrofitting the whole codebase.
Build regression suites for fast-changing code
Use change history and incident data to focus tests where AI-assisted changes are most frequent.
Test authorization and edge cases
Cover missing access checks, unusual inputs and error paths.
Questions buyers ask
Does AI-generated code need different testing?
The core techniques are the same, but the emphasis shifts. Volume rises, so automated gates matter more. Reviewers also cannot assume the author understood the requirement. Teams should check intended behavior, security, and edge cases closely, because generated code can look clean while still being wrong.
Can AI write its own tests?
It can draft them quickly, and that is useful. The risk is that tests generated from the code confirm current behavior, including its bugs. Have a person check tests against the requirement, and use methods such as mutation testing to see whether the tests would actually catch a defect.
How do you test a vibe-coded app before launch?
Start with the critical user journeys, then authentication, access control, payments, and data handling. Run security scans and check for exposed secrets. Add regression tests around whatever must not break. Finally, test on real browsers and devices, and try unexpected inputs that a quick prototype probably never handled.