Tag
11 articles tagged #Testing.
P.01100% code coverage tells you every line ran during your tests. It says nothing about whether your tests would notice if that line broke. Mutation testing answers that question directly, here's how to run it with Stryker and mutmut and what to do with the score.
P.02QA is about risk thinking, not script writing. This guide covers what a strong QA engineer brings to a team, what to test for in an interview, and how to avoid hiring someone who just runs regression suites.
P.03Comparing LangSmith, Braintrust, and Weights & Biases Weave for LLM evaluation. What each platform does well, where it breaks down, and how to build a minimum viable eval pipeline.
P.04AI tools write code fast. TDD asks you to slow down and write tests first. These two impulses seem to be in tension. Here's how they actually work together.
P.05Most E2E test suites start fast and grow painful. Playwright solves most of that, but only if you set it up right from the start. Here is what actually works in production.
P.06k6 is a developer-friendly load testing tool with JavaScript scripting, CI integration, and clear metrics. Here's how to write meaningful load tests, interpret the results, and catch performance regressions before they reach production.
P.07Vitest runs faster, works natively with ESM and TypeScript, and uses the same API as Jest. If you're still on Jest in a Vite-based project, here's the case for switching and exactly how to do it.
P.08Most E2E test suites break as fast as the features they cover. Here's how to write Playwright tests that hold up when the UI changes, the data changes, and the team keeps shipping.
P.09Unit tests tell you if your code does what you wrote. They don't tell you if your AI feature does what users need. Here's how to build an evaluation pipeline that catches the failures that matter before users do.
P.10We automated visual regression testing, test generation, and bug triage with AI. Here are the real results after 6 months — including what still needs humans.
P.11Traditional test suites break when outputs are non-deterministic. Here's how we test AI-powered features — from LLM output validation to regression testing for prompt changes, with real frameworks and examples.