Chaos Engineering
Fault injection testing means deliberately introducing specific failures — network partitions, CPU pressure, disk failures, process kills — to verify that a system handles them correctly. Chaos Mesh, Litmus, and Gremlin are the leading tools for this. Each targets different environments and failure types. Choosing the right one depends on your infrastructure,
Testing
Fault injection is the practice of deliberately introducing failures into a system to verify that it handles them correctly. Unlike chaos engineering's broad experiments, fault injection is surgical — you inject a specific failure into a specific component and observe what happens. This guide covers the core patterns for
Testing
Most security bugs in smart contracts aren't found by reading code — they're found by systematically breaking assumptions. A function you expected to always return a positive number returns zero under specific conditions. An invariant you assumed held under normal conditions breaks when two calls happen in
FastAPI
Unit tests verify individual functions. Integration tests verify that your application works when all the pieces are connected. For FastAPI applications, integration testing means running your endpoints against real databases, real caches, and real external services — not mocks. This is where you find the bugs that only appear in production:
Testing
Unit tests tell you your contract logic is correct in isolation. Integration tests tell you it works with the real DeFi ecosystem — live Uniswap pools, actual AAVE markets, production Chainlink price feeds. You need both. Forked network testing lets you run your contracts against a snapshot of mainnet state without
Web3
Gas optimization in Solidity occupies a strange position: everyone knows it matters, but few teams treat gas costs as something to test and track. The result is contracts that ship with unnecessary gas costs, regressions that sneak in during refactoring, and "optimizations" that make no measurable difference. Treating
FastAPI
FastAPI's dependency injection system is one of its most powerful features — and one of the most important to test correctly. When you override dependencies in tests, you control exactly what your endpoints receive, making tests fast, deterministic, and free of external side effects. This guide covers every pattern
FastAPI
Database testing is where most FastAPI applications get their testing strategy wrong. The common mistakes: using a shared test database that accumulates state, running migrations on every test, or — worst of all — hitting the production database in CI. This guide shows you how to test FastAPI applications backed by SQLAlchemy
FastAPI
Authentication bugs are the most expensive bugs you can ship. A missing authorization check or a JWT validation flaw can expose every user's data or let attackers elevate their privileges. This guide covers systematic testing of every auth pattern FastAPI supports, with enough depth to catch the edge
FastAPI
FastAPI is built on top of Starlette and runs on an ASGI server. Most of its interesting behavior — middleware, database calls, background tasks — happens asynchronously. Testing this code properly requires pytest-asyncio and a solid understanding of how async test infrastructure works. This guide goes beyond the basics to cover
Testing
Property-based testing with fast-check generates random inputs for pure functions. But JavaScript applications have state — React components maintain it, Redux stores track it, caches hold it, queues process it. Model-based testing generates random sequences of operations and verifies that your system stays consistent with a simple reference
Property-Based Testing
fast-check is the leading property-based testing library for JavaScript and TypeScript. While many tutorials show basic integer and string properties, real-world use requires custom arbitraries, model-based testing, async support, and proper TypeScript typing. This post covers the advanced patterns. Why fast-check Over Other JS Options