API Testing
You can't write a test for every possible input. A string field accepts billions of values. A numeric parameter has edge cases you haven't considered. An API that looks solid against your 20 hand-written test cases might crash on the 21st input — the one a
API Testing
Writing API tests by hand means writing a lot of boilerplate: constructing URLs, setting headers, serializing request bodies, parsing responses. When the API changes, you update the tests manually and hope you caught every callsite. OpenAPI Generator eliminates that boilerplate. Given an OpenAPI spec, it generates a fully typed API
API Testing
Both Dredd and Schemathesis test whether your API implementation matches its spec. Both read OpenAPI (or API Blueprint) definitions and make real HTTP requests. Beyond that, they take fundamentally different approaches — and understanding the difference determines which one belongs in your pipeline. The Core Difference Dredd is deterministic. It reads
API Testing
API contracts are promises. Your OpenAPI specification says "this endpoint returns a userId field of type string." Every consumer of your API — mobile apps, partner integrations, frontend teams — builds against that promise. When the promise breaks silently, you get production incidents, not compiler errors. Spectral is a linter
Test Reliability
Cypress flaky tests are particularly frustrating because Cypress has automatic waiting built in — so why are tests still timing out? The answer is usually one of three things: the test is waiting for the wrong thing, network requests are unpredictable, or state leaks between tests. This post walks through each
API Testing
Most teams write code first, then document it. The API exists; you write the OpenAPI spec to describe what you built. This approach has a persistent problem: the code and the documentation diverge. Consumers get specs that don't match the real behavior. Tests get written against the implementation,
Testing
End-to-end tests are slow by nature — they launch browsers, navigate real pages, wait for network. A 400-test Playwright suite can take 20-30 minutes on a single runner. Sharding splits those tests across multiple workers and brings that down to 5-8 minutes. Here's how
Testing
pytest-xdist lets you run tests across multiple CPUs — or even multiple machines — without changing a single test. A 10-minute suite can drop to 2-3 minutes with 4 workers. Here's how to set it up correctly, including the database isolation patterns that trip up most teams.
Testing
Vitest runs tests in parallel using worker processes. How those workers are created — and how isolated they are from each other — determines your test suite's speed, stability, and compatibility with native modules. Vitest offers three pool types: threads, forks, and vmThreads. Knowing which to use can cut your
Testing
A test suite that takes 15 minutes to run is a test suite developers avoid running. The fix isn't always parallelism — often it's finding the 5% of tests consuming 60% of the time and making them faster. Here's a systematic approach to profiling and
Testing
JUnit XML is the universal language of CI/CD test reporting. Originally designed for Java's JUnit, almost every test framework can emit it — Jest, pytest, Playwright, Vitest, PHPUnit, and hundreds more. Every major CI platform knows how to parse it. Here's how to use JUnit XML
Testing
A slow test suite kills developer flow. When Jest runs 800 tests sequentially it can take 8-12 minutes. With sharding across 4 parallel GitHub Actions runners, that drops to 2-3 minutes — same compute budget, 4× faster feedback. Here's how to set it up. How Jest Sharding