veza/veza-docs/ORIGIN/ORIGIN_QUALITY_METRICS.md

ORIGIN_QUALITY_METRICS.md

📋 RÉSUMÉ EXÉCUTIF

Ce document définit toutes les métriques de qualité de la plateforme Veza. Il couvre les indicateurs de qualité du code, performance, sécurité, UX, et business avec leurs seuils, méthodes de mesure, et actions correctives. Ces métriques garantissent le maintien d'un standard de qualité élevé pendant 24 mois.

🎯 OBJECTIFS

Objectif Principal

Établir un système de métriques de qualité complet, mesurable, et actionnable pour garantir l'excellence technique, la satisfaction utilisateur, et le succès business pendant 24 mois.

Objectifs Secondaires

• Détecter les régressions rapidement (< 1 jour)
• Maintenir la dette technique sous contrôle (< 5%)
• Garantir la satisfaction utilisateur (NPS > 50)
• Optimiser les performances (< 100ms p95)
• Sécuriser la plateforme (0 vulnérabilités critiques)

📖 TABLE DES MATIÈRES

1. Code Quality Metrics
2. Performance Metrics
3. Security Metrics
4. UX Metrics
5. Business Metrics
6. Technical Debt Tracking
7. Quality Gates
8. Review Criteria
9. Success Criteria

🔒 RÈGLES IMMUABLES

1. Metrics DOIVENT être mesurables (automatiquement, si possible)
2. Thresholds DOIVENT être définis (red/yellow/green)
3. Dashboards OBLIGATOIRES (Grafana, Datadog, etc.)
4. Alerting OBLIGATOIRE (PagerDuty, Slack)
5. Weekly review (équipe engineering)
6. Monthly report (stakeholders)
7. Quality gates BLOQUANTS (CI/CD pipeline)
8. Regression tests (automatiques après chaque deployment)
9. Metrics retention (1 an minimum)
10. Continuous improvement (objectifs trimestriels)

1. CODE QUALITY METRICS

1.1 Test Coverage

Metric: Percentage of code covered by tests

Targets:

🔴 Red:    < 70%   (unacceptable)
🟡 Yellow: 70-79%  (needs improvement)
🟢 Green:  ≥ 80%   (acceptable)
🌟 Goal:   ≥ 90%   (excellent)

Measurement:

# Go
go test ./... -coverprofile=coverage.out
go tool cover -func=coverage.out

# Rust
cargo tarpaulin --out Html --output-dir coverage/

# TypeScript/React
npm run test:coverage

Reporting:

Tool: Codecov or Coveralls
Frequency: Every commit (CI/CD)
Dashboard: Grafana panel "Test Coverage Trend"
Alert: Coverage drops > 5% → Slack notification

Action if Below Target:

1. Identify untested code (coverage report)
2. Write missing tests (priority: critical paths)
3. Code review: Reject PRs that decrease coverage
4. Goal: Return to ≥ 80% within 2 sprints

1.2 Cyclomatic Complexity

Metric: Measure of code complexity (number of decision paths)

Targets:

🟢 Green:  1-10   (simple, easy to test)
🟡 Yellow: 11-20  (moderate, needs attention)
🔴 Red:    > 20   (complex, refactor recommended)

Measurement:

# Go
gocyclo -over 10 .

# Rust
cargo-complexity

# TypeScript
npx complexity-report src/

Reporting:

Tool: SonarQube
Frequency: Daily (scheduled scan)
Dashboard: "Complexity Hot Spots"
Alert: New functions > 20 → Code review required

Action if Above Target:

1. Refactor function (extract methods)
2. Simplify logic (reduce nested conditions)
3. Split into smaller functions
4. Add unit tests for each path

1.3 Code Duplication

Metric: Percentage of duplicated code

Targets:

🟢 Green:  < 3%    (acceptable)
🟡 Yellow: 3-5%    (needs attention)
🔴 Red:    > 5%    (unacceptable)

Measurement:

# SonarQube (all languages)
sonar-scanner -Dsonar.projectKey=veza

# jscpd (JavaScript/TypeScript)
npx jscpd src/

Action if Above Target:

1. Identify duplicated blocks (SonarQube report)
2. Extract to shared functions/components
3. Create utility modules
4. Document patterns for reuse

1.4 Code Churn

Metric: Number of times files are modified

Targets:

🟢 Green:  < 5 changes/week/file
🟡 Yellow: 5-10 changes/week/file
🔴 Red:    > 10 changes/week/file (unstable code)

Measurement:

# Git analysis
git log --pretty=format: --name-only --since="1 week ago" | \
  sort | uniq -c | sort -rn | head -20

Reporting:

Tool: Code Climate
Dashboard: "Churn vs Complexity" matrix
Alert: High churn + high complexity → Refactor candidate

Action if Above Target:

1. Investigate reason for high churn
2. Stabilize APIs/interfaces
3. Improve test coverage (prevent regressions)
4. Refactor if needed

1.5 Static Analysis Issues

Metric: Number of linter warnings/errors

Targets:

🟢 Green:  0 errors, 0-5 warnings
🟡 Yellow: 0 errors, 6-10 warnings
🔴 Red:    Any errors OR > 10 warnings

Measurement:

# Go
golangci-lint run

# Rust
cargo clippy -- -D warnings

# TypeScript
npm run lint

Reporting:

Tool: GitHub Actions (CI/CD)
Frequency: Every commit
Dashboard: "Lint Issues Trend"
Alert: Any errors → PR blocked

Action if Above Target:

1. Fix all errors (highest priority)
2. Fix warnings (by priority level)
3. Configure linter rules (if too strict)
4. Auto-fix with formatters where possible

2. PERFORMANCE METRICS

2.1 API Response Time

Metric: Time to respond to API requests

Targets:

           p50      p95      p99
🟢 Green:  <50ms    <100ms   <500ms
🟡 Yellow: 50-100ms 100-200ms 500ms-1s
🔴 Red:    >100ms   >200ms   >1s

Measurement:

Tool: Prometheus + Grafana
Query:
  histogram_quantile(0.95, 
    rate(http_request_duration_seconds_bucket[5m])
  )

Reporting:

Dashboard: "API Performance"
Alert: p95 > 200ms for 5 minutes → PagerDuty alert

Action if Above Target:

1. Identify slow endpoints (APM traces)
2. Optimize database queries (add indexes, reduce N+1)
3. Add caching (Redis)
4. Scale horizontally (more instances)
5. Profile code (pprof for Go, flamegraph for Rust)

2.2 Database Query Performance

Metric: Query execution time

Targets:

           p50     p95     p99
🟢 Green:  <10ms   <50ms   <100ms
🟡 Yellow: 10-20ms 50-100ms 100-200ms
🔴 Red:    >20ms   >100ms  >200ms

Measurement:

-- PostgreSQL
SELECT 
  query,
  mean_exec_time,
  calls
FROM pg_stat_statements
ORDER BY mean_exec_time DESC
LIMIT 20;

Reporting:

Tool: pg_stat_statements + Datadog
Dashboard: "Slow Queries"
Alert: Query > 100ms executed > 100 times/min → Slack

Action if Above Target:

1. Analyze query plan (EXPLAIN ANALYZE)
2. Add missing indexes
3. Rewrite query (optimize joins)
4. Consider denormalization
5. Add materialized view

2.3 Frontend Performance (Lighthouse Score)

Metric: Google Lighthouse score (0-100)

Targets:

           Performance  Accessibility  Best Practices  SEO
🟢 Green:  ≥ 90         ≥ 90           ≥ 90            ≥ 90
🟡 Yellow: 80-89        80-89          80-89           80-89
🔴 Red:    < 80         < 80           < 80            < 80

Measurement:

# Lighthouse CI
npm install -g @lhci/cli
lhci autorun --collect.url=https://veza.io

Reporting:

Tool: Lighthouse CI + GitHub Actions
Frequency: Every deployment
Dashboard: "Lighthouse Scores Trend"
Alert: Score drops > 10 points → Deployment review

Action if Below Target:

Performance:
- Optimize images (WebP, lazy loading)
- Code splitting (dynamic imports)
- Reduce bundle size (tree shaking)
- Enable CDN caching
- Optimize fonts (preload, subset)

Accessibility:
- Fix color contrast issues
- Add ARIA labels
- Ensure keyboard navigation
- Add alt text to images

Best Practices:
- Use HTTPS
- Remove console.log
- Fix CSP violations
- Update dependencies

SEO:
- Add meta tags
- Improve page titles
- Add structured data
- Fix broken links

2.4 Time to First Byte (TTFB)

Metric: Server response time

Targets:

🟢 Green:  < 200ms
🟡 Yellow: 200-500ms
🔴 Red:    > 500ms

Measurement:

Tool: Cloudflare Analytics, Datadog RUM
Metric: navigation.timing.responseStart - navigation.timing.requestStart

Action if Above Target:

1. Enable server-side caching
2. Optimize database queries
3. Use CDN (Cloudflare)
4. Enable HTTP/2 or HTTP/3
5. Reduce server processing time

2.5 Streaming Performance

Metric: Audio buffering and startup time

Targets:

Initial buffering:   < 1 second
Rebuffering rate:    < 0.5%
Audio start time:    < 2 seconds

Measurement:

// Client-side measurement
const startTime = performance.now();
audio.addEventListener('canplaythrough', () => {
  const loadTime = performance.now() - startTime;
  sendMetric('audio_load_time', loadTime);
});

Reporting:

Tool: Custom metrics → Prometheus
Dashboard: "Streaming Performance"
Alert: Rebuffering rate > 1% → Engineering review

Action if Above Target:

1. Optimize audio encoding (adaptive bitrate)
2. Use CDN for audio files
3. Implement prefetching
4. Reduce initial chunk size
5. Monitor CDN performance

3. SECURITY METRICS

3.1 Vulnerability Count

Metric: Number of known vulnerabilities

Targets:

🟢 Green:  0 critical, 0 high
🟡 Yellow: 0 critical, 1-3 high
🔴 Red:    Any critical OR > 3 high

Measurement:

# Dependency scanning
npm audit
cargo audit
go list -json -m all | nancy sleuth

Reporting:

Tool: Snyk, Dependabot, Trivy
Frequency: Daily (automated scan)
Dashboard: "Vulnerability Dashboard"
Alert: Critical vulnerability → Immediate fix (< 24h)

Action if Above Target:

1. Review vulnerability details
2. Update dependency (if patch available)
3. Apply workaround (if no patch yet)
4. Remove dependency (if possible)
5. Investigate affected code paths

3.2 Failed Login Attempts

Metric: Number of failed login attempts

Targets:

🟢 Green:  < 1% of total logins
🟡 Yellow: 1-5% of total logins
🔴 Red:    > 5% of total logins (potential attack)

Measurement:

SELECT 
  DATE(attempted_at) AS date,
  COUNT(*) FILTER (WHERE success = false) AS failed,
  COUNT(*) AS total,
  ROUND(100.0 * COUNT(*) FILTER (WHERE success = false) / COUNT(*), 2) AS failure_rate
FROM login_attempts
WHERE attempted_at > NOW() - INTERVAL '7 days'
GROUP BY DATE(attempted_at);

Reporting:

Tool: Prometheus + Grafana
Dashboard: "Login Metrics"
Alert: Failure rate > 10% for 5 minutes → Security review

Action if Above Target:

1. Check for credential stuffing attack
2. Implement CAPTCHA (if not already)
3. Rate limit login endpoint
4. Block suspicious IPs (WAF rules)
5. Notify users of suspicious activity

3.3 Mean Time to Remediate (MTTR) Vulnerabilities

Metric: Time from vulnerability discovery to fix deployed

Targets:

🟢 Green:  < 7 days (critical), < 30 days (high)
🟡 Yellow: 7-14 days (critical), 30-60 days (high)
🔴 Red:    > 14 days (critical), > 60 days (high)

Measurement:

Formula: 
MTTR = (Date Fixed - Date Discovered) for all vulnerabilities / Total count

Reporting:

Tool: Jira, GitHub Security Advisories
Dashboard: "Security MTTR"
Review: Monthly security meeting

Action if Above Target:

1. Prioritize security fixes
2. Allocate dedicated time (security sprint)
3. Improve scanning frequency
4. Automate dependency updates (Dependabot)

3.4 Security Incidents

Metric: Number of security incidents

Targets:

🟢 Green:  0 incidents/quarter
🟡 Yellow: 1-2 incidents/quarter (minor)
🔴 Red:    Any data breach OR > 2 incidents

Measurement:

Manual tracking: Incident log (spreadsheet or Jira)
Categories: Data breach, DDoS, unauthorized access, etc.

Reporting:

Review: Post-incident review (within 7 days)
Dashboard: "Security Incidents"
Report: Quarterly security report to leadership

Action if Incident Occurs:

1. Incident response plan (containment, eradication)
2. Root cause analysis (RCA)
3. Implement preventive measures
4. Update security documentation
5. Conduct security training

4. UX METRICS

4.1 Core Web Vitals

Largest Contentful Paint (LCP):

🟢 Green:  < 2.5s
🟡 Yellow: 2.5-4s
🔴 Red:    > 4s

First Input Delay (FID):

🟢 Green:  < 100ms
🟡 Yellow: 100-300ms
🔴 Red:    > 300ms

Cumulative Layout Shift (CLS):

🟢 Green:  < 0.1
🟡 Yellow: 0.1-0.25
🔴 Red:    > 0.25

Measurement:

// Real User Monitoring (RUM)
import { getCLS, getFID, getLCP } from 'web-vitals';

getCLS(console.log);
getFID(console.log);
getLCP(console.log);

Reporting:

Tool: Google Analytics, Datadog RUM
Dashboard: "Core Web Vitals"
Alert: Any metric in red zone → Frontend optimization sprint

Action if Below Target:

LCP:
- Optimize server response time
- Preload critical resources
- Optimize images
- Remove render-blocking JS/CSS

FID:
- Break up long tasks
- Use web workers
- Defer non-critical JS
- Code splitting

CLS:
- Add size attributes to images/videos
- Avoid inserting content above existing
- Use CSS transforms (not top/left)

4.2 Accessibility Score (WCAG)

Metric: WCAG 2.1 compliance level

Targets:

🟢 Green:  AAA compliance (no violations)
🟡 Yellow: AA compliance (1-5 minor violations)
🔴 Red:    < AA compliance (> 5 violations)

Measurement:

# Automated testing
npm install -g @axe-core/cli
axe https://veza.io --save violations.json

Reporting:

Tool: axe DevTools, Lighthouse
Frequency: Every deployment
Dashboard: "Accessibility Violations"
Alert: New violations introduced → Block merge

Action if Below Target:

1. Fix critical violations (color contrast, missing labels)
2. Add ARIA attributes
3. Ensure keyboard navigation
4. Add screen reader support
5. Manual testing with assistive technology

4.3 User Satisfaction (NPS)

Metric: Net Promoter Score

Targets:

🟢 Green:  NPS > 50   (excellent)
🟡 Yellow: NPS 30-50  (good)
🔴 Red:    NPS < 30   (needs improvement)

Calculation:

NPS = % Promoters (9-10) - % Detractors (0-6)

Example:
50% Promoters, 10% Detractors
NPS = 50 - 10 = 40

Measurement:

Survey: In-app survey (quarterly)
Question: "How likely are you to recommend Veza to a friend?" (0-10)
Sample size: Min 100 responses

Reporting:

Dashboard: "User Satisfaction"
Review: Quarterly product review
Trend: Track NPS changes over time

Action if Below Target:

1. Analyze detractor feedback (qualitative data)
2. Identify top pain points
3. Prioritize fixes (product roadmap)
4. Follow up with detractors (close the loop)
5. Measure improvement (next survey)

4.4 Error Rate (User-Facing)

Metric: Percentage of user actions that result in errors

Targets:

🟢 Green:  < 0.1%
🟡 Yellow: 0.1-1%
🔴 Red:    > 1%

Measurement:

// Frontend error tracking
window.addEventListener('error', (event) => {
  sendErrorToSentry(event.error);
});

// React error boundary
componentDidCatch(error, errorInfo) {
  logErrorToService(error, errorInfo);
}

Reporting:

Tool: Sentry, Datadog
Dashboard: "Error Rate"
Alert: Error rate spikes > 5x baseline → Immediate investigation

Action if Above Target:

1. Identify most common errors (Sentry dashboard)
2. Reproduce errors (local testing)
3. Fix root cause
4. Add error handling (graceful degradation)
5. Add monitoring for specific errors

5. BUSINESS METRICS

5.1 Conversion Rate

Metric: Percentage of visitors who complete desired action

Targets:

Sign-up conversion:       > 5%
Free → Paid conversion:   > 3%
Cart → Purchase:          > 10%

Measurement:

Formula:
Conversion Rate = (Conversions / Total Visitors) * 100

Example:
1,000 visitors, 50 sign-ups
Conversion = (50 / 1,000) * 100 = 5%

Reporting:

Tool: Google Analytics, Mixpanel
Dashboard: "Conversion Funnel"
Review: Weekly growth meeting

Action if Below Target:

1. A/B test landing page variants
2. Optimize sign-up flow (reduce friction)
3. Improve value proposition messaging
4. Add social proof (testimonials, reviews)
5. Optimize pricing page

5.2 Customer Lifetime Value (CLV)

Metric: Total revenue from average customer

Targets:

🟢 Green:  CLV > $500
🟡 Yellow: CLV $200-$500
🔴 Red:    CLV < $200

Calculation:

CLV = Average Purchase Value × Purchase Frequency × Customer Lifespan

Example:
Average purchase: $50
Purchases/year: 4
Lifespan: 3 years
CLV = $50 × 4 × 3 = $600

Reporting:

Dashboard: "Customer Metrics"
Review: Monthly business review
Cohort analysis: Track CLV by acquisition channel

Action if Below Target:

1. Increase purchase frequency (email campaigns)
2. Increase average order value (upselling, bundles)
3. Improve retention (loyalty program)
4. Reduce churn (better onboarding)

5.3 Churn Rate

Metric: Percentage of customers who cancel subscription

Targets:

🟢 Green:  < 5%/month
🟡 Yellow: 5-10%/month
🔴 Red:    > 10%/month

Calculation:

Monthly Churn Rate = (Customers Lost This Month / Customers at Start of Month) * 100

Example:
Start: 1,000 customers
Lost: 50 customers
Churn = (50 / 1,000) * 100 = 5%

Reporting:

Tool: ChartMogul, internal analytics
Dashboard: "Churn Analysis"
Review: Weekly retention meeting

Action if Above Target:

1. Analyze churn reasons (exit surveys)
2. Identify at-risk users (usage patterns)
3. Implement retention campaigns
4. Improve onboarding (reduce early churn)
5. Add features to increase stickiness

5.4 Monthly Active Users (MAU)

Metric: Number of unique users who engage with platform monthly

Targets:

🟢 Green:  MAU growth > 10%/month
🟡 Yellow: MAU growth 0-10%/month
🔴 Red:    MAU declining

Calculation:

MAU = COUNT(DISTINCT user_id) 
      WHERE last_activity_at >= DATE_SUB(NOW(), INTERVAL 30 DAY)

Reporting:

Dashboard: "User Engagement"
Metric: MAU, DAU, DAU/MAU ratio (stickiness)
Review: Weekly growth meeting

Action if Below Target:

1. Improve retention (email re-engagement)
2. Increase acquisition (marketing campaigns)
3. Add viral features (sharing, referrals)
4. Improve product value (new features)
5. Reduce friction (optimize UX)

6. TECHNICAL DEBT TRACKING

6.1 Technical Debt Ratio

Metric: Percentage of development time spent on technical debt

Targets:

🟢 Green:  < 5%
🟡 Yellow: 5-10%
🔴 Red:    > 10%

Measurement:

Formula:
TD Ratio = (Time Spent on TD / Total Development Time) * 100

Track in Jira:
- Label "technical-debt" on tickets
- Measure time spent (burndown)

Reporting:

Dashboard: "Technical Debt"
Review: Quarterly tech review
Visualization: Debt heat map (by module)

Action if Above Target:

1. Allocate dedicated sprints for debt reduction
2. Prioritize high-impact debt (ROI analysis)
3. Refactor instead of patching
4. Improve code reviews (prevent new debt)
5. Set debt reduction goals (quarterly)

6.2 TODO Comments

Metric: Number of TODO comments in code

Targets:

🟢 Green:  < 50 TODOs
🟡 Yellow: 50-100 TODOs
🔴 Red:    > 100 TODOs

Measurement:

# Count TODO comments
grep -r "TODO" --include="*.go" --include="*.rs" --include="*.ts" . | wc -l

Reporting:

Tool: SonarQube
Dashboard: "Code Debt Indicators"
Review: Monthly tech debt review

Action if Above Target:

1. Convert TODOs to Jira tickets
2. Prioritize and schedule fixes
3. Remove completed TODOs
4. Add context to remaining TODOs (why, when)

6.3 Deprecated Code

Metric: Lines of code marked as deprecated

Targets:

🟢 Green:  < 1% of codebase
🟡 Yellow: 1-3% of codebase
🔴 Red:    > 3% of codebase

Measurement:

# Count @deprecated annotations
grep -r "@deprecated" --include="*.go" --include="*.rs" --include="*.ts" . | wc -l

Action if Above Target:

1. Plan migration (deprecation timeline)
2. Communicate to users (if public API)
3. Provide alternatives (migration guide)
4. Remove after grace period (6 months)

7. QUALITY GATES

7.1 CI/CD Quality Gates

Pre-Merge Checks (blocking):

✅ All tests pass (unit, integration)
✅ Test coverage ≥ 80%
✅ No linter errors
✅ No security vulnerabilities (critical/high)
✅ Code review approved (2+ reviewers)
✅ No merge conflicts

Pre-Deployment Checks (blocking):

✅ All CI tests pass
✅ Smoke tests pass (staging)
✅ Performance tests pass (load, stress)
✅ Security scan pass (SAST, DAST)
✅ Database migrations successful
✅ Rollback plan documented

Post-Deployment Checks (monitoring):

✅ Error rate < baseline (+10%)
✅ Response time < baseline (+20%)
✅ CPU/Memory usage < 80%
✅ No critical alerts (30 min observation)

7.2 Release Quality Gates

Criteria for Production Release:

✅ All acceptance tests pass
✅ Security audit complete (no critical issues)
✅ Performance benchmarks met
✅ Accessibility audit pass (WCAG AA)
✅ Documentation updated
✅ Release notes written
✅ Rollback tested (staging)
✅ On-call engineer assigned
✅ Stakeholder approval

7.3 Feature Flag Quality Gates

Criteria for Enabling Feature:

✅ Feature tested (QA sign-off)
✅ Monitoring dashboards created
✅ A/B test configured (if applicable)
✅ Gradual rollout plan defined
✅ Rollback procedure documented
✅ User documentation published

8. REVIEW CRITERIA

8.1 Code Review Checklist

Functionality:

• Code does what it's supposed to do
• Edge cases handled
• Error handling complete

Tests:

• Unit tests included
• Tests cover edge cases
• Tests are readable

Code Quality:

• Code follows style guide
• No code duplication
• Functions < 50 lines
• Complexity < 10

Security:

• Input validated
• SQL injection prevented
• XSS prevented
• Secrets not committed

Performance:

• No N+1 queries
• Efficient algorithms
• Caching considered

Documentation:

• Public functions documented
• README updated (if needed)
• Comments explain "why" (not "what")

8.2 Design Review Criteria

UI Design:

• Consistent with design system
• Responsive (mobile, tablet, desktop)
• Accessible (WCAG AA minimum)
• Follows brand guidelines

UX Design:

• User flow intuitive
• Feedback on user actions
• Error states handled
• Loading states shown

8.3 Architecture Review Criteria

Scalability:

• Horizontally scalable
• No single point of failure
• Handles expected load (+ 50%)

Security:

• Follows security best practices
• Data encrypted (at rest, in transit)
• Authentication/authorization correct

Maintainability:

• Clear separation of concerns
• Dependencies documented
• Deployment strategy defined

9. SUCCESS CRITERIA

9.1 Feature Success Criteria

Template:

Feature: [Feature Name]

Success Metrics:
- [ ] Adoption: > [X]% of users try feature within 30 days
- [ ] Engagement: > [Y]% of users who try feature use it weekly
- [ ] Satisfaction: NPS > [Z] (feature-specific survey)
- [ ] Performance: Feature response time < [A]ms (p95)
- [ ] Quality: < [B] critical bugs reported

Business Impact:
- [ ] Increase conversion by [X]%
- [ ] Reduce churn by [Y]%
- [ ] Increase revenue by $[Z]

Timeline:
- Launch: [Date]
- Review: [Date + 30 days]
- Decision: Keep / Iterate / Sunset

Example (Track Collaboration Feature):

Feature: Real-time Track Collaboration

Success Metrics:
- [ ] Adoption: > 10% of creators try collaboration within 30 days
- [ ] Engagement: > 50% of users who try it use it weekly
- [ ] Satisfaction: NPS > 60
- [ ] Performance: < 100ms latency (p95)
- [ ] Quality: < 5 critical bugs

Business Impact:
- [ ] Increase premium subscriptions by 15%
- [ ] Reduce creator churn by 20%

Timeline:
- Launch: 2025-12-01
- Review: 2026-01-01
- Decision: Based on metrics

9.2 Sprint Success Criteria

Definition of Done (Sprint):

✅ All planned stories completed
✅ No critical bugs in production
✅ Test coverage maintained (≥ 80%)
✅ All code reviewed and merged
✅ Documentation updated
✅ Demo prepared (for stakeholders)
✅ Retrospective completed

9.3 Project Success Criteria

Large Project (Multi-Sprint):

✅ All epics completed
✅ Acceptance criteria met
✅ User documentation published
✅ Training materials created (if needed)
✅ Performance benchmarks met
✅ Security audit passed
✅ Stakeholder sign-off
✅ Post-launch monitoring (30 days)
✅ Lessons learned documented

✅ CHECKLIST DE VALIDATION

Metrics Defined

• All metrics have clear definitions
• All metrics have measurable targets (red/yellow/green)
• All metrics have measurement methods
• All metrics have reporting dashboards
• All metrics have alerting configured

Quality Gates

• CI/CD gates configured
• Pre-merge checks automated
• Pre-deployment checks automated
• Post-deployment monitoring active

Review Process

• Code review checklist documented
• Design review criteria defined
• Architecture review criteria defined

Success Criteria

• Feature success criteria template created
• Sprint DoD defined
• Project success criteria defined

📊 MÉTRIQUES DE SUCCÈS (Meta-Metrics)

Metrics Compliance

• Metrics Coverage: 100% of critical areas have defined metrics
• Metrics Freshness: Dashboards updated real-time (< 5 min lag)
• Alert Response Time: < 15 minutes for critical alerts
• Quality Gate Compliance: 100% of releases pass quality gates

🔄 HISTORIQUE DES VERSIONS

Version	Date	Changements
1.0.0	2025-11-02	Version initiale - Métriques de qualité complètes

---

⚠️ AVERTISSEMENT

CES MÉTRIQUES SONT IMMUABLES

Les métriques de qualité définies ici sont VERROUILLÉES. Toute modification nécessite:

1. RFC Quality Metrics Change avec justification
2. Approbation VP Engineering
3. Update dashboards et alerting
4. Communication à l'équipe

Les quality gates sont BLOQUANTS et ne peuvent être contournés.

---

Document créé par: Engineering Leadership + DevOps
Date de création: 2025-11-02
Prochaine révision: Trimestrielle
Propriétaire: VP Engineering

Statut: ✅ APPROUVÉ ET VERROUILLÉ