Writing Tests That Actually Help

Beyond Coverage Numbers

Chasing 100% test coverage is a trap. A codebase can have perfect coverage and still be riddled with bugs if the tests only verify trivial behavior. The goal is not to test more — it is to test better.

Test Behavior, Not Implementation

Tests that mirror the internal structure of the code break every time you refactor, even when behavior stays the same. Instead, write tests from the perspective of the caller:

• What inputs does this function accept?
• What outputs or side effects does it produce?
• What edge cases matter?

This approach makes tests resilient to internal changes while still catching regressions.

The Arrange-Act-Assert Pattern

Structure every test clearly:

1. Arrange — set up the preconditions and inputs.
2. Act — execute the behavior under test.
3. Assert — verify the expected outcome.

A test that is hard to read is a test that will be skipped or deleted when it fails.

When to Use Mocks (and When Not To)

Mocks are useful for isolating the unit under test from slow or unreliable dependencies like networks or databases. But over-mocking leads to tests that pass even when the real integration is broken.

Prefer fakes or in-memory implementations for data stores. Use mocks sparingly and only at system boundaries.

Flaky Tests Are Worse Than No Tests

A test that fails randomly erodes trust in the entire suite. When a flaky test appears, fix it immediately or delete it. Never let the team learn to ignore red builds.

Write the Test You Wish You Had

When you find a bug in production, write the test that would have caught it before fixing the code. This practice steadily hardens your suite against real-world failures.