Testing AI-Generated Code: Strategies That Actually Work

Why AI Code Needs Different Testing

AI models make different mistakes than humans:

• Hallucinated APIs that don't exist
• Edge cases ignored or wrong
• Security patterns from outdated training data
• Subtle type mismatches

The AI Testing Framework

1. Contract Testing

Verify the AI's code matches your spec:

• Input/output types match
• Error cases handled as specified
• No unexpected side effects

2. Mutation Testing

AI often writes code that passes tests but is wrong:

• Use mutation testing tools
• If tests don't catch mutations, tests are weak

3. Property-Based Testing

AI struggles with invariants:

• "This should always be true"
• Use hypothesis (Python) or fast-check (JS)

4. Integration Testing

AI code in isolation ≠ AI code in your app:

• Test with real database
• Test with actual API calls
• Test with production-like data

5. Security Scanning

Non-negotiable for AI code:

• Snyk for dependencies
• Semgrep for custom rules
• OWASP ZAP for web apps

Golden Rule

Test AI code more rigorously than human code, not less.