mock-strategy-guide

Mocks isolate your Rust tests by replacing real dependencies like databases or HTTP services with controlled implementations. Use trait-based abstractions to define interfaces, then implement mock versions that return predefined data, enabling fast, reliable unit tests without infrastructure.

Verification-enabled mocks track interactions—method calls and their arguments—during test execution. After running test code, assert that your mock received expected calls with specific parameters, catching integration bugs without touching real systems.

Controlled-failure mocks let you inject specific errors into your test scenarios on demand. Configure mocks to return errors, timeouts, or invalid states, then verify your code handles each case correctly without depending on actual infrastructure failures.

Builder-pattern mocks provide a fluent API to configure mock behavior before tests run. Chain methods to set return values, failure modes, and expectations, then pass the configured mock to your async code—simplifying complex test setups across hexagonal architectures.

Yes. Mocks integrate seamlessly with standard Rust testing frameworks and async runtimes. They're compatible with trait-based repository and service patterns, letting you use familiar testing tools without additional dependencies or platform-specific workarounds.

Use mocks when you need fast feedback, isolated test cases, or cannot easily provision test infrastructure. Mocks excel at unit testing business logic; integration tests verify the real system end-to-end. Combine both—mocks for speed and coverage, integration tests for confidence.