QA Team Workflow Guide - AI-SDLC v3.2.0

🎯 Complete QA Testing Workflow with AI-SDLC Automation

This guide provides step-by-step instructions for QA engineers to leverage AI-SDLC tools for comprehensive testing automation and quality assurance.

📋 QA Daily Workflow

1. Pull Request Testing

Step 1: Validate PR has automated tests

# Check PR test status
gh pr view <PR-number>
gh pr checks <PR-number>

Step 2: Run comprehensive test validation

# Full test suite with coverage
npm run test:coverage

# E2E tests (automatically run on PRs)
npm run test:e2e

# Security validation
./scripts-complex/security-scanner.js quick

Line-by-line PR validation:

1. Check GitHub Actions status (green checkmarks required)
2. Verify E2E tests ran automatically (only on PRs)
3. Confirm coverage meets thresholds (80/80/70)
4. Review test failure screenshots if any red X's
5. Approve only if all automated checks pass

2. Manual Testing Coordination

Before manual testing, validate automation coverage:

# Check which areas have test coverage
npm run test:coverage -- --reporter=html
open coverage/index.html

Generate missing tests for uncovered areas:

# AI-generate tests for specific functionality
./ai-sdlc generate-from-requirements "Test credit dispute submission with required documentation validation"

# Generate E2E tests for user workflows
./ai-sdlc generate-from-requirements "End-to-end credit report request with FCRA compliance checks"

3. Credit Repair Domain Testing

FCRA/FACTA Compliance Validation:

# Generate compliance-specific tests
./ai-sdlc generate-from-requirements "Validate FCRA Section 604 permissible purpose disclosure"
./ai-sdlc generate-from-requirements "Test secure PII data handling in credit reports"
./ai-sdlc generate-from-requirements "Validate credit score calculation with FICO 8 algorithm"

Expected automated coverage:

• Credit score capping at 850
• PII data encryption validation
• Consumer consent verification
• Permissible purpose compliance
• Dispute submission workflows

4. Test Environment Management

Set up test environment:

# Initialize AI test generation
./ai-sdlc test-init

# Validate environment setup
./ai-sdlc doctor

# Run environment-specific tests
npm run test:e2e -- --project=staging

Database testing (existing database integration):

# Test against existing PostgreSQL database
./scripts-complex/postgres-automation.sh test

# Validate database connections
./ai-sdlc validate

🔧 QA-Specific Tool Usage

Automated Test Generation for QA

Command-line test creation:

# Generate tests from natural language requirements
./ai-sdlc generate-from-requirements "User should be able to update their credit monitoring preferences"

# Convert manual test cases to automated
./ai-sdlc convert-manual-to-auto <qase-test-case-id>

# Auto-heal existing broken tests
./ai-sdlc heal-and-generate

Batch test generation:

# Generate comprehensive test coverage
./ai-sdlc auto-complete-testing

Visual Testing and Debugging

E2E test debugging workflow:

# Run E2E with visual debugging
npm run test:e2e-headed

# Check failure artifacts
ls test-results/
open test-results/screenshots/
open test-results/videos/

Line-by-line E2E debugging:

1. Run npm run test:e2e-headed
2. Watch browser execute tests in real-time
3. If test fails, check test-results/ folder
4. Review screenshots for UI issues
5. Watch videos to understand failure sequence
6. Report bugs with visual evidence attached

Test Coverage Analysis

Coverage validation workflow:

# Generate detailed coverage report
npm run test:coverage -- --reporter=lcov --reporter=html

# View interactive coverage report
open coverage/index.html

Coverage quality gates (automatic enforcement):

• Lines: 80% minimum (enforced)
• Functions: 80% minimum (enforced)
• Branches: 70% minimum (enforced)

When coverage is insufficient:

1. Identify uncovered areas in HTML report
2. Generate missing tests: ./ai-sdlc test-gen <uncovered-file>
3. Verify new tests increase coverage
4. Repeat until thresholds met

Performance Testing Integration

Performance monitoring:

# Run performance analysis
./ai-sdlc perf monitor

# Generate performance report
./ai-sdlc perf report

Performance validation checklist:

• [ ] Page load times under 3 seconds
• [ ] API response times under 500ms
• [ ] Credit report generation under 2 seconds
• [ ] Database queries optimized (check logs)

📊 QA Metrics and Reporting

Test Automation Metrics

Daily metrics to track:

# Test execution summary
npm run test:coverage -- --reporter=json > test-metrics.json

# E2E test results
playwright show-report

Key metrics:

• Test coverage percentage (target: 80/80/70)
• E2E test pass rate (target: 98%+)
• Test execution time (should be 60% faster with smart testing)
• Bug detection rate (target: 92%+)

Automated Reporting

Generate QA reports:

# Comprehensive test report
./scripts-complex/dev-utils.js generate-test-report

# Send report to MS Teams (if configured)
./scripts-complex/webhook-manager.js send-test-report test-results.json

🚨 QA Issue Resolution Workflow

Test Failures

When automated tests fail:

1. Check GitHub Actions logs:

gh run list
gh run view <run-id>

1. Analyze failure type:
2. Unit test failure: Code logic issue
3. E2E test failure: UI/UX issue

4. Coverage failure: Insufficient test coverage

5. Gather evidence:

# For E2E failures
ls test-results/screenshots/
ls test-results/videos/

1. Create bug report with:
2. Screenshot/video evidence
3. Console logs
4. Steps to reproduce
5. Expected vs actual behavior

Auto-Healing Test Maintenance

When UI changes break E2E tests:

# Trigger auto-healing
./ai-sdlc heal-and-generate

# Check healing results
grep -r "auto-healing" tests/e2e/

Auto-healing features:

• Smart selector fallback
• Dynamic element detection
• Automatic test maintenance
• Self-healing statistics reporting

🔄 Integration with Development Workflow

PR Review Process

QA validation checklist for PRs:

• [ ] All GitHub Actions checks pass
• [ ] E2E tests executed (automatic on PRs)
• [ ] Coverage meets thresholds (80/80/70)
• [ ] No security vulnerabilities detected
• [ ] Credit repair compliance tests pass

PR approval workflow:

1. Check automated test status
2. Review test coverage report
3. Validate E2E test results
4. Confirm security scan passed
5. Approve if all automated checks pass

Release Testing

Pre-release validation:

# Full test suite
npm run test:coverage

# E2E regression testing
npm run test:e2e

# Security audit
./scripts-complex/security-scanner.js scan

# Performance validation
./ai-sdlc perf monitor

📈 Credit Repair Specific QA

Compliance Testing

FCRA compliance validation:

• Permissible purpose verification
• Consumer consent documentation
• Accurate credit reporting
• Dispute process compliance

FACTA compliance validation:

• Identity verification processes
• Secure document handling
• Fraud alert management
• Credit monitoring capabilities

Data Security Testing

PII protection validation:

# Generate security-focused tests
./ai-sdlc generate-from-requirements "Validate PII data encryption in credit reports"
./ai-sdlc generate-from-requirements "Test secure customer authentication flow"

🔗 QA Team Resources

Documentation Links

• Developer Workflow Guide - Understanding dev process
• Code Reviewer Guide - Review process integration
• Troubleshooting Guide - Common issue resolution
• Scripts Reference - Complete command documentation
• Testing Documentation - Comprehensive testing guide

Emergency Contacts

When automation fails:

1. Check Troubleshooting Guide
2. Run ./ai-sdlc doctor for diagnostics
3. Contact development team with error logs
4. Document issues for framework improvement

🎯 QA Success Metrics

Target outcomes with AI-SDLC:

• 80% reduction in manual QA time
• 92% automated bug detection rate
• 98%+ E2E test reliability
• 60% faster test execution
• 100% FCRA/FACTA compliance coverage