Testing
Real-Time Operating Systems introduce concurrency, scheduling, and timing constraints that make testing significantly harder than single-threaded firmware. This guide covers testing strategies for FreeRTOS, Zephyr, and VxWorks: verifying task timing and deadlines, detecting race conditions and priority inversion, testing memory management in constrained environments, using SystemView for execution tracing, and
Testing
JTAG and GDB together give embedded engineers precise, non-invasive control over firmware execution on real hardware. This guide covers JTAG protocol fundamentals, connecting via OpenOCD, remote GDB debugging, breakpoints and watchpoints for test validation, inspecting memory and peripheral registers, and scripting GDB with Python to run automated test assertions in
Testing
Hardware-in-the-Loop (HIL) testing connects real electronic control units to simulated physical environments, letting engineers validate firmware behavior under realistic conditions without building the actual vehicle, aircraft, or machine. This guide explains what HIL is, how it differs from software-in-the-loop and processor-in-the-loop approaches, the hardware and software components of a typical
Testing
DeFi protocols operate in an adversarial environment where attackers use flash loans, oracle manipulation, and sandwich attacks to drain value. Testing must simulate these attacks explicitly. Mainnet forking provides realistic state, Foundry's invariant tester verifies that core mathematical properties hold across arbitrary transaction sequences, and explicit attack simulations
Testing
The Controller Area Network (CAN) bus is the backbone of automotive electronics: every modern vehicle carries tens of ECUs communicating over it. Testing those ECUs requires understanding the CAN protocol, the diagnostic layer (UDS/ISO 14229), the tools available for sniffing and simulation, and how to automate test scenarios that
Testing
Embedded systems testing requires a different mindset than application testing — hardware may not be available, timing is critical, and failures can be physically dangerous. This guide covers unit testing embedded C/C++ with Unity and CppUTest, mocking hardware registers, emulating targets with QEMU, and wiring everything into a CI pipeline
Testing
Testing firmware on Raspberry Pi and microcontrollers bridges the gap between host-based unit tests and full hardware integration testing. This post covers pytest-embedded for Espressif targets, MicroPython test patterns, GPIO and serial interface testing, mocking hardware peripherals with a secondary controller, and building CI/CD pipelines that execute tests on
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. Key Takeaways Unit test business logic in
Testing
Testing Lambda functions requires separating business logic from the Lambda handler. Unit-test the business logic with Jest or pytest (no AWS SDK needed), integration-test the handler against LocalStack or real AWS, and mock AWS SDK calls using jest.mock() or moto. The key insight: the Lambda handler is a thin
Testing
Zendesk's REST API is comprehensive and well-documented, making automated testing straightforward. This guide covers testing ticket workflows, triggers, macros, webhooks, and third-party integrations — with CI-ready patterns for test isolation and cleanup. Why Test Zendesk Configurations? Zendesk accounts accumulate configuration debt: triggers, automations, macros, and views pile up over
Testing
Off-the-shelf eval frameworks (DeepEval, Ragas, TruLens) cover 80% of use cases. For the remaining 20% — specialized domains, proprietary metrics, internal tooling constraints — you need to build your own. This guide walks you through designing metrics, curating datasets, implementing an LLM judge, and wiring it all into CI. When to Build
Testing
ETL testing verifies three things: all expected data was extracted (completeness), transformations produced correct output (accuracy), and the data was loaded without corruption or duplication (integrity). Each phase requires different testing techniques: reconciliation counts and checksums for extraction, sample-based assertions for transformation, and row count plus constraint checks for loading.