Regression Gates & Advanced Workflow Automation

This guide layers deterministic governance on top of profiling + AI-assisted analysis. It converts insights into objective pass/fail signals for CI.

Gate Types

Gate Purpose Typical Threshold
CPU Hotspot Regression Prevent large flat% increases > +8% flat delta
Allocation Regression Control alloc_space growth > +10% flat delta
Benchmark Slowdown Maintain micro performance > +5% time/op delta
Overall Rating Fail Enforce quality bar rating overall < 5
New Hotspot Alert (soft) Detect churn new hotspot > 4% flat

Enabling Hard Gates

Set environment variables in the analysis job:

AI_GATE_ENABLE=true
AI_GATE_CPU_REG_MAX=8
AI_GATE_MEM_REG_MAX=10
AI_GATE_BENCH_SLOW_MAX=5
AI_GATE_MIN_RATING=5

(Analyzer will be extended to exit non-zero when violated.)

Suggested PR Flow

  1. Dev pushes code → profiling + bench jobs run.
  2. AI analyzer produces structured report.
  3. Gates evaluate JSON → pass/fail.
  4. PR comment includes summarised reasons for any failure.
  5. Optional: label PR with performance-regression automatically (GitHub Action).

Speedscope Integration (Optional)

Add conversion step:

pprof -sample_index=cpu -proto generator_cpu.prof > cpu.pb
npx speedscope cpu.pb  # or use a converter library

Then upload cpu.speedscope.json artifact.

Automation Tips

Enhancement Benefit
Cache baseline profiles Lower noise; compute diff vs stable artifact
Nightly trend job Track historical rating & hotspots
Slack webhook notification Real-time visibility
Issue auto-open Forces accountability for persistent failures

Tuning Strategy

  • Start with soft gates (warnings) for 1–2 weeks.
  • Record false positives; adjust thresholds or sampling sizes.
  • Promote to hard gates once stable.

Example JSON Snippet (report.json)

{
  "cpu_improvements": ["runtime.mallocgc: 12.5% -> 7.1% (5.4% improvement)"],
  "cpu_regressions": ["main.process: 8.0% -> 16.4% (8.4% regression)"],
  "rating": {"cpu":7,"memory":5,"overall":6},
  "summary": "Overall net improvement..."
}

Use jq in a gate step:

CPU_REG_MAX=${AI_GATE_CPU_REG_MAX:-8}
COUNT_REG=$(jq '.cpu_regressions | length' report.json)
[ "$COUNT_REG" -gt 0 ] && echo "CPU regressions detected: $COUNT_REG" || true

Future Roadmap

  • Span ↔ hotspot correlation for endpoint-level gating.
  • Automated root cause hypothesis generation.
  • Performance budget dashboards (Grafana + JSON data source).

These practices turn profiling from raw data into a governed performance lifecycle, preventing regressions while enabling rapid, verifiable optimizations.

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