Testing
Database Seeding and Test Data Management at Scale
At 10 tests, test data management is easy. You have a few fixture files, maybe a before_each that resets a table or two, and everything works.
Testing
At 10 tests, test data management is easy. You have a few fixture files, maybe a before_each that resets a table or two, and everything works.
Testing
SLOs and SLAs describe what your system promises. Tests verify whether those promises hold.
Security Testing
The average Node.js application has over 1,000 transitive dependencies. The average Python project has hundreds.
Security Testing
Web application security testing with Burp is intuitive when there's a browser to proxy.
Testing
Integration tests are slow, brittle, and expensive to maintain. You spin up multiple services, wire them together in a test environment, and hope that everything behaves the way it did last Tuesday. When a test fails, you spend twenty minutes figuring out whether the problem is in the consumer, the
Testing
Flaky tests are a design problem, not a tooling problem. The most sophisticated CI infrastructure won't make a fundamentally non-deterministic test reliable. The solution is to design tests so they can only produce one result: pass when the application works correctly, fail when it doesn't.
Testing
Example-based testing asks "does this specific input produce this specific output?" Property-based testing asks "does this output have this characteristic for all possible inputs?" Property testing generates hundreds of random examples and finds the edge cases you wouldn't think to write manually.
Embedded Testing
Real-time operating systems introduce a class of bugs that are nearly invisible in manual testing: priority inversions, stack overflows, missed deadlines, and race conditions between tasks. Most teams discover these in the field, after a product has shipped. With the right testing approach, you can catch them in CI
Test Reliability
Flaky tests don't fix themselves. Left unaddressed, they multiply — developers copy patterns from flaky tests, shared infrastructure accumulates timing assumptions, and eventually the CI pipeline becomes a game of "rerun until green." This is a practical playbook for detecting flakiness, quarantining failing tests without abandoning coverage,
Test Automation
RTOS-based firmware has a reputation for being untestable. Tasks run concurrently, interrupts fire asynchronously, queues link components together, and the whole system only makes sense when everything is running simultaneously on real hardware. But "untestable" mostly means "nobody has tried systematically." With the right techniques,
Desktop Testing
Desktop application testing sits in an awkward position in most organizations: less investment than web testing, harder to automate, and often maintained by teams that haven't had to think about desktop UI in years. Yet desktop applications — enterprise tools, developer utilities, internal software — remain critical for many businesses.
Test Automation
Desktop app CI is harder than web app CI. You need real operating systems — not just Linux containers — because your app runs on Windows, macOS, and Linux with platform-specific behavior at every layer: native menus, file dialogs, WebView implementations, code signing, and installer formats. GitHub Actions provides hosted runners