Performance Regression Detection - Quantified Speed

Purpose

Establish baseline performance metrics, detect regressions >10%, and analyze historical trends. Calculates regression score (0.00-1.00) showing performance health. Integrates with Serena MCP for continuous alerting and trend visualization.

When to Use

• Detecting performance regressions before deployment
• Tracking performance improvements over time
• Comparing performance across branches
• Identifying slow components needing optimization
• Setting performance SLAs and monitoring compliance
• Alerting on regressions >10% threshold

Core Metrics

Regression Score:

Score = Current / Baseline × 1.0 (capped at 1.00)
- 1.00 = same or better than baseline (green)
- 0.95 = 5% slower (acceptable)
- 0.90 = 10% slower (warning threshold)
- 0.80 = 20% slower (critical)

Benchmark Tracking Example:

Component: API Request Handler
├─ Baseline (main): 45ms
├─ Current (feature): 48ms (6.7% slower) → Score: 0.97
├─ Threshold: >50ms regression
├─ Trend: Stable (+0.1% vs last week)
└─ Status: ✅ Pass (within tolerance)

Component: Database Query
├─ Baseline: 120ms
├─ Current: 132ms (10% slower) → Score: 0.90
├─ Trend: +4% slower over 2 weeks (regressing)
└─ Status: ⚠️ Warning (at threshold, monitor)

Workflow

Phase 1: Baseline Establishment

1. Establish baseline: Benchmark main branch performance
2. Sample size: Run ≥10 iterations for stability
3. Record stats: Store mean, std dev, min, max
4. Push to Serena: Create historical baseline

Phase 2: Continuous Monitoring

1. Run benchmarks: On each commit/PR
2. Compare to baseline: Calculate regression percentage
3. Calculate score: 0.00-1.00 metric
4. Alert if >10%: Trigger warning/critical notifications
5. Push to Serena: Send metrics for tracking

Serena Push Format:

{
  "metric_type": "performance_regression",
  "project": "task-app",
  "component": "api_handler",
  "baseline_ms": 45,
  "current_ms": 48,
  "regression_percent": 6.7,
  "regression_score": 0.97,
  "status": "PASS",
  "timestamp": "2025-11-20T12:00:00Z"
}

Phase 3: Trend Analysis

1. Track history: 30-day regression score trend
2. Detect patterns: Consistent degradation vs spikes
3. Correlate with commits: Identify which changes caused regression
4. Forecast: Predict if trends continue
5. Alert on trajectory: Flag if regressing >0.02/week

Trend Analysis Example:

Performance Trend (30 days)
1.00 ┤     ╭─────────────┐
0.95 ┤    ╱               ╲     ↓ Regression detected
0.90 ┤───╱                 ╰──── Commit 3f7d2c added 15% overhead
0.85 ┤                          Optimized in 4f9a1e, back to 0.97
     └────────────────────────
       7d   14d   21d   28d

Action: Optimization identified regressor, restored health

Real-World Impact

Web Application Response Time:

• Baseline: 150ms p95
• Regression detected: 170ms (13% slower, score 0.87)
• Serena alert triggered, investigation found N+1 query
• Fixed in 2 hours, restored to 152ms (score 0.99)
• Prevented customer-facing slowdown

Mobile App Load Time:

• Tracking 5 key paths (Launch, Login, Dashboard, Search, Checkout)
• Regression detected in Search: 450ms → 540ms (score 0.83)
• Trend analysis showed gradual 2-week degradation
• Root cause: Missing cache invalidation in recent refactor
• Fix deployed, score back to 1.02 (10% faster!)

Success Criteria

✅ Baseline established for critical paths ✅ Regression score ≥0.90 maintained ✅ Serena alerts fire within 5 min of regression ✅ Historical trend shows improvement or stability ✅ No unexplained spikes >0.05 score variance ✅ All regressions >10% investigated and documented