I break software before your users do.

✅ SLA gate — perf tests that can fail the build

Latency budgets live in slas.yml, per endpoint. A script parses Locust's CSV, prints a verdict table into the GitHub job summary, and exits non-zero on any breach. “A dashboard needs a human — an exit code scales.”

🧠 Memory-leak hunter

Samples container memory under constant load, discards warm-up, fits a least-squares trend — and fails only when both growth rate and total growth blow their budgets. Demonstrated against a simulated ~50 KB/request leak:

⚡ Load shapes as ~15 lines of code

Stress, spike and soak profiles are LoadTestShape classes selected by one env var — one reviewed workload model serves every scenario.

class SpikeShape(LoadTestShape):
    """20-user baseline, sudden 10× spike."""
    def tick(self):
        t = self.get_run_time()
        if t < 120: return (20, 5)
        if t < 180: return (200, 50)  # the spike
        if t < 360: return (20, 50)   # recovery
        return None

📉 Regression detection, run over run

One run tells you where you are; comparing runs tells you where you're heading. Nightly CI diffs endpoint-by-endpoint and fails on p95 growth, throughput drops, or worsening error rate:

🐳 Distributed by default

make up WORKERS=8 — Locust master/worker split in plain Docker Compose, FastHttpUser for ~10× RPS per generator. Same images scale across hosts or Kubernetes.

📊 Live observability

Pre-provisioned Grafana dashboard: RPS, p50/p95 overlaid with the user ramp, failures/s, and the leak-hunting memory panel — problems visible the moment they start, not in a PDF afterwards.

👥 Realistic workload model

75% browsers, 25% buyers running the full funnel as a SequentialTaskSet; catch_response marks a 200-without-order_id as a failure — wrong answers delivered quickly are still wrong.

🩹 Self-healing locator engine

Every element is an ElementDefinition with ordered fallback candidates. When the primary selector dies mid-day (booking.com A/B tests constantly), candidates are polled round-robin, the winner is logged as a healing event, and non-volatile heals are persisted to a git-committed store so future runs try them first.

destinationInput: {
  key: 'stays.searchbox.destinationInput',
  candidates: [
    'input[name="ss"]',                            // primary
    '[data-testid="destination-container"] input', // fallback 1
    'input[placeholder*="Where are you going"]',   // fallback 2
  ],
}

📡 A real heal, captured in CI

Worker processes append events to their own files; a custom reporter consolidates them after the run. The engine is itself covered by E2E tests.

{
  "key": "flights.results.flightCards",
  "failed": ["[data-testid=…_card]"],
  "healed": "[aria-label^='Result item']",
  "persistPreferred": true
}

🩹 healed → logged → persisted → suite stays green

🌍 Live-site quirks, engineered around

• Flights served as Kayak white-label in some regions — variant auto-detected, stable deep-link scheme used
• Sign-in popup at random moments — addLocatorHandler auto-dismisses whenever it blocks
• /flights/ redirect trap — navigation enters via the header tab, like a real user
• A/B DOM swaps — absorbed by the healer, variants kept as candidates

🧱 Layered architecture

Plus a data layer with fluent builders — dates always generated relative to “now”, so the suite never rots from hardcoded test data.

♿ Beyond the happy path

axe-core WCAG 2.0 A/AA scan gating on critical violations, toMatchAriaSnapshot structural assertions, and APIRequestContext canaries at the HTTP level.

⚙️ CI built to scale

Static checks fail fast → sharded test matrix → blob reports merged into one HTML report. PR annotations, nightly cron, manual dispatch with tag filter. Scaling = extending the shard matrix.

🏷 14 tests, 9 suites, 3 browsers

Tag-driven: @smoke @stays @flights @cars @attractions @a11y @api @healer compose with --grep regexes. Verified green against the live site, Chromium / Firefox / WebKit.