Unit tests, integration tests, end-to-end tests, test-driven development, property-based testing, and AI-assisted test generation. The testing course for engineers who want their tests to prevent production incidents — not just pass CI.
This is a text-first course that links out to the best supporting material on the internet instead of trying to replace it. The goal is to make this the best course on testing you can find — even without producing a single minute of custom video.
This course is built by engineers who ship testing systems in production. It reflects how these tools actually behave at scale.
Every day includes working code examples you can copy, run, and modify right now. Understanding comes through doing.
Instead of re-explaining existing documentation, this course links to the definitive open-source implementations and the best reference material on testing available.
Each day is designed for about an hour of focused reading plus hands-on work. Do the whole course over a week of lunch breaks. No live classes, no quizzes.
Each day stands alone. Read them in order for the full picture, or jump straight to the day that answers the question you have today.
What makes a test valuable (isolation, repeatability, speed), Jest and Pytest for JavaScript and Python, arrange/act/assert pattern, test doubles (mocks, stubs, spies), and the test anti-patterns that produce false confidence.
Integration test strategies, database integration tests with test containers, API contract testing with Pact, the testing pyramid and why your ratio of unit/integration/E2E tests matters for CI speed.
Playwright setup and browser automation, page object model for test maintainability, screenshot comparison testing, network interception for mocking backends, and running E2E tests in CI without flakiness.
The red-green-refactor cycle, when TDD improves design and when it slows you down, TDD for complex business logic vs UI code, refactoring safely with tests, and the TDD workflow with AI code generation tools.
Using Claude and GitHub Copilot to generate tests, property-based testing with Hypothesis and fast-check, mutation testing to measure test quality, and building a test generation pipeline that keeps coverage high with minimal manual effort.
Instead of shooting our own videos, we link to the best deep-dives already on YouTube. Watch them alongside the course. All external, all free, all from builders who ship this stuff.
Complete unit testing courses in JavaScript (Jest) and Python (Pytest) — AAA pattern, mocking, and test organization.
Browser automation with Playwright — setup, selectors, page object model, and running E2E tests in CI without flakiness.
The TDD cycle demonstrated on real code — when it improves design and when to skip it.
Unit vs integration vs E2E test ratios, test execution speed, and the testing strategy that catches bugs without slowing CI to a crawl.
Using GitHub Copilot, Claude, and Cursor to generate tests — what works, what needs review, and building a workflow around AI test generation.
The best way to deepen understanding is to read the canonical open-source implementations. Clone them, trace the code, understand how the concepts in this course get applied in production.
The Jest testing framework source. The /packages directory shows how mocking, assertion, and test runner work under the hood.
The pytest source. The plugin system makes pytest the most extensible Python testing framework — the /src/pytest directory shows how fixtures and test collection work.
The Playwright source. Reading the browser automation implementation shows how Playwright controls Chrome, Firefox, and WebKit.
Property-based testing for Python. Hypothesis generates test cases that find edge cases your manual tests miss — the best way to find bugs in complex logic.
If your tests all pass but production still breaks, your test strategy has gaps. This course identifies and fixes them.
The goal isn't 100% coverage — it's catching the bugs that matter before they reach users. This course teaches the testing strategy that achieves that efficiently.
Testing AI outputs requires different strategies — evaluation frameworks, snapshot testing, and the prompt regression approaches that catch LLM behavior changes.
The 2-day in-person Precision AI Academy bootcamp covers software quality and AI-assisted testing in depth — hands-on, with practitioners who build AI systems for a living. 5 U.S. cities. $1,490. 40 seats max. June–October 2026 (Thu–Fri).
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