How Accurate Are AI Test Results?
AI test results are probabilistic, not deterministic. The same exploration might find different issues or take different paths. AI testing is best for finding obvious problems quickly, not for guaranteeing 100% accuracy. Always verify findings manually.
Accuracy Characteristics
Probabilistic, Not Deterministic
AI test results are probabilistic:
- Same input ≠ Same output - Running same exploration twice may produce different results
- Different paths - AI might explore different routes each time
- Similar but not identical - Results will be similar, but not exactly the same
This is different from scripted tests (Playwright, Selenium) which are deterministic - same input always produces same output.
Best at Finding Obvious Issues
AI testing excels at finding:
- Obvious bugs - Things that clearly look broken
- Visual problems - Layout issues, overlapping elements
- Console errors - JavaScript errors that break functionality
- Network errors - Failed API requests
AI testing struggles with:
- Subtle bugs - Edge cases, nuanced problems
- Logic errors - Business logic mistakes
- Performance issues - Slow but not broken functionality
- Design preferences - "This looks ugly" is subjective
False Positives
What Are False Positives?
False positives are issues reported that aren't actually problems:
- AI flags something as broken, but it's not
- Visual issue reported, but design is intentional
- Error flagged, but it's expected behavior
Common False Positives
- Intentional design - AI thinks something looks wrong, but it's by design
- Expected errors - Errors that are supposed to happen (validation, etc.)
- Dynamic content - Content that changes, AI flags as inconsistent
- Third-party elements - External content that behaves differently
How to Handle False Positives
- Review findings - Always manually verify reported issues
- Check if intentional - Determine if "issue" is actually intended behavior
- Ignore if false - Don't fix things that aren't actually broken
- Learn patterns - Notice which types of false positives are common
False Negatives
What Are False Negatives?
False negatives are problems that exist but aren't detected:
- Bug exists, but AI didn't find it
- Issue present, but AI didn't explore that path
- Problem exists, but AI didn't notice it
Why False Negatives Happen
- Didn't explore that path - AI took different route, missed the issue
- Not obvious enough - Problem too subtle for AI to detect
- Edge case - Requires specific conditions AI didn't trigger
- Logic error - Business logic mistake AI can't detect
How to Handle False Negatives
- Don't rely solely on AI - Still do manual testing
- Test critical paths manually - Verify important flows yourself
- Use AI as supplement - AI finds obvious issues, you find subtle ones
- Accept limitations - AI won't catch everything, and that's okay
Accuracy Expectations
What to Expect
- Finds obvious issues reliably - Broken layouts, console errors, etc.
- May miss subtle issues - Edge cases, logic errors
- Some false positives - Always verify findings manually
- Different results each run - Probabilistic, not deterministic
What NOT to Expect
- 100% accuracy - Will miss some issues, flag some non-issues
- Deterministic results - Same input won't always produce same output
- Complete coverage - Won't test every possible scenario
- Perfect understanding - May misinterpret some situations
Improving Accuracy
Better Instructions
Provide clear, specific instructions:
- "Test the checkout flow" vs "explore the site"
- Focus AI on specific areas
- Clarify what's important to check
Manual Verification
Always verify findings:
- Review screenshots and logs
- Manually reproduce issues
- Determine if findings are real problems
Multiple Runs
Run exploration multiple times:
- Different runs may find different issues
- Increases chance of catching problems
- Helps identify patterns
Best Practices
- Verify all findings - Don't blindly trust AI results
- Use for quick checks - Best for finding obvious issues fast
- Supplement with manual testing - AI + manual testing = better coverage
- Accept limitations - AI won't catch everything, and that's okay
- Focus on confidence - Goal is confidence before shipping, not perfect coverage