Testing at Scale

01 Scaling Playwright‑BDD to 3,000+ Tests Without Losing Your Mind How we structured fixtures, steps, and tags to keep a large Behaviour‑Driven test suite fast, readable, and maintainable. Read article → 02 Highly Scalable Test Data Management in Playwright How we tie thousands of test users and hundreds of journey datasets to the right test — automatically — using fixtures, not glue code. Read article → 03 Playwright Fixtures as a Runtime System: Five Patterns Beyond `page` Scopes, teardown‑only guards, cached sessions, and self‑built browser contexts — the fixture mechanics that quietly run a 3,000‑test suite. Read article → 04 Tag‑Driven Test Execution: Turning 3,000 Playwright Tests Into a Schedulable Pipeline How a small tag vocabulary and a "suite‑as‑data" model let us slice thousands of tests into precisely‑tuned, parallelisable batches. Read article → 05 Running Playwright in the Cloud: A Reusable CI/CD Pipeline with Docker, Terraform & AWS One container image, one suite‑as‑data definition, and an infrastructure‑as‑code pipeline that fans thousands of tests across the cloud — portable to GitHub Actions, GCP, or anywhere else. Read article → 06 Time‑Travel Testing in Playwright: Automating Date‑Sensitive Flows Some bugs only appear on the last day of the month, on a leap year, or 30 days after sign‑up. Here's how to make those moments reproducible by controlling the clock — and how to put time back when the test ends. Read article → 07 Don't Just Test the Screen: Verifying Database & Message Side Effects in Playwright A green UI assertion can hide a broken backend. Here's how to drive the front end with Playwright and then assert on the database and message store to prove the *system* actually did the right thing. Read article → 08 Beyond Mocking: Capturing Real Network Traffic and Validating Response Contracts Most teams use request interception to *fake* responses. The more powerful use is the opposite — let the real traffic flow, capture it, and assert that the page actually called the right APIs and got the right answers. Read article → 09 Accessibility Testing at Scale: From One‑Off axe Script to a Tagged, Gated, Reported Suite A single `axe-core` script in a corner of the repo is easy to ignore. Wiring accessibility checks into the *same* tag‑driven, fixture‑backed, reported pipeline as your functional tests is what makes them actually stick. Read article → 10 Quality Gates Beyond Functional: Lighthouse and K6 in One Tag‑Driven Pipeline Functional tests tell you the feature works. They say nothing about whether the page is *fast enough* or the API *holds up under load*. Here's how performance and budget checks become first‑class, tagged, gated members of the same suite — not separate tools nobody runs. Read article → 11 One Suite, Many Brands: A White‑Label / Multi‑Tenant Testing Strategy When the same product ships under several brands, the naive answers are "copy the suite per brand" or "test only one and hope." Both are wrong. Here's how a single suite covers many tenants through shared journeys, a layered data‑resolution fallback, and per‑tenant overrides. Read article →