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How to Use GitScrum for Performance Optimization Projects?
How to use GitScrum for performance optimization projects?
Manage performance work in GitScrum with dedicated labels, include performance budgets in acceptance criteria, and track improvements with measurable goals. Document baselines and optimizations in NoteVault. Performance teams with structured workflow achieve 40% faster applications [Source: Performance Engineering Research 2024].
Performance workflow:
- Measure - Baseline metrics
- Analyze - Identify bottlenecks
- Prioritize - By impact
- Optimize - Make improvements
- Test - Verify improvement
- Document - Record results
- Monitor - Ongoing tracking
Performance labels
| Label | Purpose |
|---|---|
| type-performance | All performance work |
| perf-backend | Server performance |
| perf-frontend | Client performance |
| perf-database | Database performance |
| perf-network | Network optimization |
| perf-memory | Memory optimization |
| perf-critical | High priority |
Performance metrics
| Metric | Area |
|---|---|
| LCP | Largest Contentful Paint |
| FID | First Input Delay |
| CLS | Cumulative Layout Shift |
| TTFB | Time to First Byte |
| TTI | Time to Interactive |
| Response time | API latency |
| Throughput | Requests per second |
NoteVault performance documentation
| Document | Content |
|---|---|
| Performance budget | Target metrics |
| Baseline report | Current state |
| Optimization log | Changes made |
| Testing procedures | How to measure |
| Architecture | Performance patterns |
Performance task template
## Performance: [description]
### Baseline
- Metric: [which metric]
- Current value: [measurement]
- Target value: [goal]
### Analysis
- Bottleneck: [identified issue]
- Root cause: [why slow]
### Optimization
- Approach: [strategy]
- Changes: [what to change]
### Verification
- [ ] Performance test run
- [ ] Meets target
- [ ] No regressions
### Results
- Before: [value]
- After: [value]
- Improvement: [%]
Performance columns
| Column | Purpose |
|---|---|
| Backlog | All performance work |
| Profiling | Analysis phase |
| Development | Optimization |
| Perf Testing | Verification |
| Monitoring | Ongoing tracking |
Profiling tasks
| Task Type | Focus |
|---|---|
| CPU profiling | CPU bottlenecks |
| Memory profiling | Memory leaks |
| Network profiling | Network latency |
| Database profiling | Query optimization |
| Load testing | Scalability |
Quick wins vs big projects
| Type | Characteristics |
|---|---|
| Quick win | Low effort, immediate impact |
| Medium project | Moderate effort, good impact |
| Big project | High effort, significant impact |
Performance testing checklist
| Test | Verify |
|---|---|
| Baseline | Before measurement |
| After optimization | Improvement |
| Regression | No degradation |
| Load | Under stress |
| Edge cases | Worst scenarios |
Performance budgets
| Budget | Example |
|---|---|
| Page size | < 1MB |
| JavaScript | < 200KB |
| LCP | < 2.5s |
| TTFB | < 200ms |
| API latency | < 100ms p95 |
Common performance issues
| Issue | Solution |
|---|---|
| Slow queries | Index, optimize |
| Large bundles | Code splitting |
| Memory leaks | Profiling |
| Network latency | Caching, CDN |
| Rendering | Lazy loading |
Performance monitoring
| Practice | Implementation |
|---|---|
| RUM | Real user monitoring |
| Synthetic | Automated tests |
| APM | Application monitoring |
| Alerts | Performance regression alerts |
Performance metrics tracking
| Metric | Track |
|---|---|
| Core Web Vitals | User experience |
| Response times | API performance |
| Error rates | Reliability |
| Throughput | Capacity |