Test automation tutorials, frameworks, and tools — from first scripts to full CI/CD pipelines.
Test Automation
Test Chrome extensions at three levels: unit-test business logic with Jest + chrome-mock (no browser required), integration-test UI with Puppeteer by loading the unpacked extension, and end-to-end test user flows with HelpMeTest. Use sinon-chrome or jest-chrome to mock the chrome.* APIs in unit tests.
Testing
"Should we test on real devices or emulators?" is a false choice. The right answer is both — but for different purposes and at different stages of your testing pipeline. Understanding what each approach catches and misses helps you build a mobile testing strategy that's thorough without
Test Automation
Integration tests that use mocked databases lie. They test your code's interaction with a mock, not with the actual database engine. When your ORM generates slightly wrong SQL, or when PostgreSQL's behavior differs from H2's, or when Redis's Lua scripting has a
Test Automation
PIT (Pitest) is the standard mutation testing tool for Java. It integrates with Maven and Gradle, runs mutations on your bytecode (faster than source-level mutation), and generates HTML reports showing exactly which mutations survived and where. Maven Setup Add the PIT plugin to pom.xml: <build> <
Test Automation
.NET has a mature test automation ecosystem built around three layers: unit tests (xUnit/NUnit/MSTest + Moq), integration tests (WebApplicationFactory, Testcontainers), and end-to-end tests (Playwright, Selenium). This guide covers setting up all three layers, structuring a test project, running tests in CI, and measuring coverage. Key Takeaways Three-
Testing
Code coverage measures the percentage of your source code executed during tests. It's measured by tools like Istanbul/NYC (JavaScript), Coverage.py (Python), and JaCoCo (Java). Coverage is a useful signal for finding untested code, but it's frequently misused as a quality gate. 100% coverage doesn&
QA
The testing pyramid is a framework for balancing test types: many unit tests at the base, fewer integration tests in the middle, and a small number of E2E tests at the top. This distribution optimizes for speed, reliability, and coverage. Most teams fail by inverting the pyramid — writing too many
Testing
Test fixtures are the foundation every test stands on. Write them well and your tests are fast, isolated, and easy to debug. Write them carelessly and you'll spend more time fixing tests than writing code. This guide covers the principles and patterns that separate reliable fixtures from the
Testing
Test data management is the practice of creating, maintaining, and controlling the data your tests depend on. It's one of the most underestimated parts of a test suite — and when done poorly, it causes more test failures than bad code. This guide covers the full landscape: what test
Testing
Test data factories eliminate the boilerplate of constructing test objects. Instead of manually building up every field of every object in every test, you define a factory once — with sensible defaults — and tests only specify what matters. This guide covers factory patterns in JavaScript from scratch, then looks at dedicated
Testing
Mock data generation is how you create realistic test data without using real production data. It covers everything from simple in-memory objects to large datasets, API response mocks, and schema-driven generators. This guide covers the tools, strategies, and decisions behind effective mock data generation for software testing. What
Testing
factory_boy is the standard Python library for test factories. It integrates directly with Django's ORM, SQLAlchemy, MongoEngine, and plain Python classes — and it removes the boilerplate of constructing test objects one field at a time. This guide covers everything from basic factories to advanced patterns used in