Testing Anti Patterns

Mock-centric tests verify mock interactions rather than actual code behavior, creating false positives. Identify them by checking if assertions target mock objects or stub methods directly; refactor to test the real output and side effects your code produces instead.

Test-only code in production creates maintenance burden, obscures intent, and risks accidental misuse. Keep production code clean by moving test configuration and fixture setup outside the main codebase; use dependency injection and separate test doubles.

Mock dependencies to isolate the unit under test and verify behavior without side effects. Before mocking, understand what the dependency does; if mocking reveals coupling or unclear contracts, that signals a deeper architectural issue worth addressing first.

Brittle tests over-assert on implementation details like component structure or mock calls. Refactor by testing observable behavior and outcomes instead; verify what the code does, not how it does it, and use integration tests for cross-layer concerns.

Yes, anti-patterns are framework-agnostic. The core issues—mock overuse, test-only production code, incomplete fixtures, and poor isolation—apply across Jest, Vitest, pytest, RSpec, Go testing, and JUnit; recognize and refactor them consistently.

Low test confidence often stems from anti-patterns: tests asserting on mocks rather than behavior, missing edge cases, or incomplete mock data. Apply coherent fixture design, enforce TDD discipline, and verify tests catch real regressions in actual code paths.