Testing AI-Generated Code: Our QA Process

An inside look at how we ensure code quality when AI agents are doing the development.

The Challenge

AI-generated code can be impressive—or impressively broken. How do you ensure quality without human code review on every line?

Our Multi-Layer Approach

Layer 1: Automated Testing

Every deployment runs:
**Unit tests**: Function-level correctness
**Integration tests**: Component interactions
**E2E tests**: Full user flows with Playwright
**Accessibility tests**: Axe-core automated checks
**Security scans**: Dependency vulnerabilities, code patterns

Minimum threshold: 90% test coverage. Typical result: 95-98%.

Layer 2: Static Analysis

Before deployment:
**TypeScript strict mode**: Type safety
**ESLint**: Code quality rules
**Prettier**: Consistent formatting
**Lighthouse**: Performance, SEO, accessibility scores

All must pass. No exceptions.

Layer 3: Deployment Validation

After deployment to staging:
**HTTP status checks**: All routes return 200
**Console error check**: Zero errors on page load
**Responsive test**: Desktop, tablet, mobile viewports
**Load time test**: First Contentful Paint < 1.5s

Layer 4: Human Spot Checks

QA AI reviews a sample:
Visual appearance
User flow logic
Content quality
Edge case handling

Not every line, but enough to catch systematic issues.

What We've Caught

Real examples from our QA process:

Engineer AI forgot error handling: Caught by E2E tests simulating API failures.

Missing mobile responsive breakpoints: Caught by viewport testing.

Accessibility violations: Caught by Axe-core (missing ARIA labels).

N+1 query performance issue: Caught by load testing.

When AI Fails

What happens when tests fail?

1. Ticket goes to "blocked" status 2. Error logs sent to Engineer AI 3. Engineer AI debugs and fixes 4. Redeployment with new tests

Average fix time: 30-60 minutes.

The 98% Rule

We aim for 98% automated test coverage, not 100%. Why?

The last 2% is expensive and brittle. It's faster to have human spot-checks than chase perfect automation.

Comparison to Traditional QA

Traditional: Manual testing, subjective evaluation, inconsistent coverage.

Our approach: Automated first, human validation second, objective metrics.

Result: Faster feedback, fewer bugs in production, measurable quality.

Client Confidence

We share all test results:
Lighthouse scores
Test coverage reports
Security scan results
Performance metrics

Clients can verify quality themselves. No "trust us"—just data.

The Future

We're exploring:
Visual regression testing (screenshot diffs)
AI-powered bug detection (analyzing patterns)
Synthetic user testing (AI simulating user behavior)

Quality assurance is evolving as fast as development. We're committed to staying ahead.