How to Test AI-Generated Code Before You Trust It
Quick Answer
You should test AI-generated code by reviewing what changed, running the app, checking core flows, testing edge cases, verifying permissions, and scanning for security risks. Never trust AI-generated code just because it looks correct; require tests, review, and clear acceptance criteria.
Why AI-Generated Code Needs Testing
AI-generated code can look clean and confident while still containing subtle bugs, missing edge cases, or security gaps. It doesn't know your full system the way a human developer who's worked on it for months does, so it can make locally reasonable choices that break something elsewhere in the app.
Start by Understanding What Changed
Before testing, read through exactly what the AI changed, not just the final result. Ask the AI to explain its changes in plain language if the diff isn't clear. Understanding the change is the first line of defense against accepting something you don't actually understand.
Run the App and Check Core Flows
After any AI-generated change, run the app and manually walk through the core user flows that could be affected, not just the specific feature that was changed. A fix in one area can unintentionally break a related flow that shares the same code path.
Test Edge Cases and Error States
Test what happens with empty input, invalid input, very large input, and missing data. AI-generated code often handles the "happy path" well but skips less obvious edge cases unless you specifically ask for them to be handled.
Check Permissions and Security
Verify that any change involving user data, authentication, or roles actually enforces the permissions it's supposed to. Ask the AI directly whether the change introduces any new security considerations, and independently verify sensitive areas like access control rather than taking its word for it.
Review Dependencies and Environment Variables
Check whether the AI added any new dependencies or packages, and confirm they're legitimate and necessary before installing them. Also check that no API keys, secrets, or credentials were hardcoded into the code instead of stored securely.
Use AI to Create Test Cases
Ask the AI to generate a list of test cases for the change, covering normal use, edge cases, and failure scenarios. This is a good way to double-check its own work, though you should still run the tests yourself rather than trusting a description of what "should" happen.
What Not to Trust Automatically
- A claim that "this fixes the issue" without actually running the code yourself
- Code that touches authentication, payments, or permissions without careful review
- New dependencies added without checking what they do
- Large, sweeping changes when a small fix should have been sufficient
- Confidence in the AI's explanation as a substitute for actually testing
AI Code Testing Checklist
- You understand what changed and why
- Core user flows have been manually tested, not just the specific feature
- Edge cases and error states have been checked
- Permissions and security-sensitive areas have been reviewed carefully
- New dependencies and environment variables have been checked
- The fix has been verified against the original failing case
Related PiSkill Resources
Use the Code Debugger & Error Fixer Prompt for structured debugging support, and the Excel Formula & Spreadsheet Helper Prompt if you're validating data-related logic changes.