Testing Strategies That Actually Work

Testing is one of those topics where everyone has an opinion and nobody agrees. Should you aim for 100% coverage? Write tests first? Mock everything?

Here’s what I’ve learned after years of building (and maintaining) test suites.

The Testing Pyramid

You’ve probably seen the testing pyramid: lots of unit tests at the bottom, fewer integration tests in the middle, and a handful of end-to-end tests at the top.

This model works because:

• Unit tests are fast and pinpoint failures. They run in milliseconds and tell you exactly which function broke.
• Integration tests verify interactions. They catch issues that unit tests miss, like mismatched interfaces between modules.
• E2E tests validate the user experience. They’re slow and brittle, but they ensure the critical paths actually work.

The ratio varies by project, but a good starting point is 70% unit, 20% integration, 10% E2E.

Write Tests for Behavior, Not Implementation

This is the single most important principle.

Bad test:

test("fetchUser calls API with correct endpoint", () => {
  const spy = jest.spyOn(api, "get");
  fetchUser("123");
  expect(spy).toHaveBeenCalledWith("/users/123");
});

Good test:

test("fetchUser returns user data for valid ID", async () => {
  const user = await fetchUser("123");
  expect(user).toEqual({ id: "123", name: "Alice" });
});

The first test breaks if you refactor the implementation. The second test only cares about the outcome. Refactor freely.

Test Edge Cases and Errors

Happy path tests are easy to write but low value. Most bugs live in edge cases:

• Empty inputs
• Null or undefined values
• Maximum/minimum boundaries
• Network failures
• Rate limits
• Concurrent operations

Error handling is especially important. Does your code fail gracefully? Do error messages help users recover?

Keep Tests Fast

Slow tests don’t get run. If your test suite takes 10 minutes, developers will skip it. Aim for sub-second unit tests and sub-minute integration tests.

Strategies:

• Mock external dependencies. Don’t hit real APIs or databases in unit tests.
• Run tests in parallel. Most test runners support this out of the box.
• Use in-memory databases. SQLite or an in-memory Postgres instance is faster than a real DB.
• Lazy-load test data. Don’t set up massive fixtures if you only need a few records.

Don’t Obsess Over Coverage

100% coverage doesn’t mean zero bugs. It means you tested every line, not every scenario.

Coverage is a useful metric for finding untested code, but it’s a terrible goal. Focus on:

• Testing critical paths (authentication, payments, data loss scenarios)
• Testing complex logic (algorithms, state machines)
• Testing code that’s changed recently

Skip testing trivial getters, setters, and framework boilerplate unless they contain logic.

Integration Tests for the Messy Parts

Some systems are hard to unit test: async workflows, third-party integrations, complex UI interactions. That’s where integration tests shine.

Example: testing a job queue. You could mock the queue and test the enqueueing logic, but does the job actually run? An integration test that enqueues a job and verifies the side effect is more valuable.

E2E Tests for Confidence

E2E tests are expensive. Use them sparingly for:

• Critical user journeys (signup, checkout, core workflows)
• Cross-browser compatibility
• Visual regression testing

Tools like Playwright and Cypress make E2E testing easier, but keep the suite small. One or two smoke tests per feature is often enough.

Test-Driven Development (TDD)

TDD—writing tests before code—has passionate advocates and detractors. I’m somewhere in the middle.

TDD works well for:

• Well-defined problems with clear inputs and outputs
• Refactoring existing code
• Bug fixes (write a failing test that reproduces the bug, then fix it)

TDD is awkward for:

• Exploratory coding (prototyping, spike solutions)
• UI work where you’re iterating on design
• Problems where you’re not sure what the API should look like

Use TDD when it helps. Don’t force it.

Conclusion

Good testing is about balance. Write enough tests to catch regressions and build confidence, but not so many that maintenance becomes a burden.

Test behavior, not implementation. Focus on critical paths and edge cases. Keep tests fast. And remember: tests are code too. They need refactoring, documentation, and care.