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Test Strategy

A testing strategy answers three questions: what to test, at what level, and how much. Without a strategy, teams end up with either too many slow, brittle e2e tests or too few tests overall - both are expensive. This skill gives the judgment to design a test suite that provides high confidence, fast feedback, and low maintenance cost.

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When to use this skill

Trigger this skill when the user:

• Asks which type of test to write for a given scenario
• Wants to design a testing strategy for a new service or feature
• Needs to decide between unit, integration, and e2e tests
• Asks about test coverage targets or metrics
• Wants to implement contract testing between services
• Is dealing with flaky tests and needs a remediation plan
• Asks about TDD or BDD workflow

Do NOT trigger this skill for:

• Writing the actual test code syntax for a specific framework (defer to framework docs)
• Performance testing or load testing strategy (separate domain)

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Key principles

1. Test behavior, not implementation - Tests should survive refactoring. If moving logic between private methods breaks your tests, the tests are testing the wrong thing. Test public contracts and observable outcomes.

2. The Testing Trophy over the pyramid - The classic pyramid (many unit, fewer integration, few e2e) was coined before modern tooling. The Trophy (Kent C. Dodds) weights integration tests most heavily: static analysis at the base, unit tests for isolated logic, integration tests for the bulk of coverage, and a few e2e tests for critical paths.

3. Fast feedback loops - A test suite that takes 30 minutes to run is a test suite that doesn't get run. Design for speed: unit tests in milliseconds, integration tests in seconds, e2e tests reserved for CI only.

4. Test at the right level - The cost of a test rises as you move up the stack (slower, more brittle, harder to debug). Test each concern at the lowest level that meaningfully exercises it.

5. Flaky tests are worse than no tests - A test that sometimes fails trains the team to ignore failures. A flaky test in CI delays every deploy. Fix or delete flaky tests immediately; never tolerate them.

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Core concepts

Test types taxonomy

Type	What it tests	Speed	Cost	Use for
Static	Type errors, lint violations	Instant	Near-zero	Type safety, obvious mistakes
Unit	Single function/class in isolation	< 10ms	Low	Pure logic, edge cases, algorithms
Integration	Multiple modules together with real dependencies	100ms-2s	Medium	Service layer, DB queries, API handlers
E2E	Full user journey through deployed stack	5-60s	High	Critical user paths, smoke tests
Contract	API contract between producer and consumer	Seconds	Medium	Microservice boundaries

The Testing Trophy

        /\
       /e2e\           - Few: critical flows only
      /------\
     /  integ  \       - Most: service + DB + API
    /------------\
   /    unit      \    - Some: pure logic and edge cases
  /----------------\
 /     static       \  - Always: types, lint, format
/--------------------\

The key insight is that integration tests give the best ROI for most application code: they test real behavior through real dependencies without the brittleness of e2e tests.

Test doubles

Use the minimum isolation necessary for the test's purpose:

Double	When to use	Risk
Stub	Replace slow/unavailable dependency, return canned data	Low - no behavior coupling
Mock	Verify a side effect was triggered (email sent, event published)	Medium - couples to call signature
Spy	Observe calls without replacing behavior	Medium - couples to call count/args
Fake	Replace infrastructure with working in-memory version	Low - tests real behavior patterns

Prefer fakes for infrastructure (in-memory DB, in-memory queue). Mocks should be reserved for side effects you cannot otherwise observe.

Coverage metrics

Metric	What it measures	When to use
Line coverage	% of lines executed	Baseline floor, not a target
Branch coverage	% of conditional paths taken	Better for logic-heavy code
Mutation coverage	% of introduced bugs caught by tests	Gold standard for test quality

Line coverage above ~80% has diminishing returns and creates perverse incentives. Mutation coverage reveals whether tests actually assert meaningful things.

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Common tasks

Choose the right test type - decision matrix

When deciding what level to test something at, apply this logic:

Is this pure logic with no external dependencies?
  YES → Unit test
  NO  → Does it require a real DB / HTTP call / file system?
          YES → Integration test (use real infrastructure or a fast fake)
          NO  → Does it span multiple services or require a browser?
                  YES → E2E test (sparingly)
                  NO  → Integration test

Additional rules:

• Cross-service API boundaries → Contract test (Pact or similar)
• Complex UI interaction that cannot be tested at component level → E2E
• Algorithm with many edge cases → Unit test per edge case + one integration

Design a test suite for a new service

Structure the test suite before writing the first line of code:

1. Map the test surface - Identify all external I/O: databases, queues, HTTP clients, file system. These are the integration seams.
2. Choose infrastructure strategy - Real DB with test containers, in-memory fake, or Docker Compose. Prefer real DBs for schema-heavy services.
3. Define the testing trophy for your context - Decide the ratio before you write tests. A typical distribution: 60% integration, 30% unit, 10% e2e.
4. Set up test data factories - Centralize how test objects are created. Factories prevent fragile fixtures and make tests self-documenting.
5. Wire CI from day one - Tests that only run locally drift. Run unit + integration in every PR, e2e in pre-merge or nightly.

Write effective unit tests - patterns

Unit tests work best for:

• Pure functions (same input always gives same output)
• Complex conditional logic with many branches
• Data transformations and parsing
• Domain model invariants

Arrange-Act-Assert structure:

test('applies 10% discount for orders over $100', () => {
  // Arrange
  const order = buildOrder({ subtotal: 120 });

  // Act
  const discounted = applyLoyaltyDiscount(order);

  // Assert
  expect(discounted.total).toBe(108);
});

Parameterize boundary conditions:

test.each([
  [99,  0],   // just below threshold - no discount
  [100, 10],  // exactly at threshold
  [200, 20],  // above threshold
])('order of $%i gets $%i discount', (subtotal, expectedDiscount) => {
  const order = buildOrder({ subtotal });
  expect(applyLoyaltyDiscount(order).discount).toBe(expectedDiscount);
});

See references/test-patterns.md for more patterns.

Write integration tests - database and API

For database integration tests:

// Use real DB, roll back after each test
beforeEach(() => db.beginTransaction());
afterEach(() => db.rollbackTransaction());

test('saves user and returns with id', async () => {
  const user = await userRepo.create({ name: 'Alice', email: 'alice@test.com' });
  expect(user.id).toBeDefined();
  const found = await userRepo.findById(user.id);
  expect(found.name).toBe('Alice');
});

For HTTP API integration tests, test the full request cycle:

test('POST /orders returns 201 with order id', async () => {
  const response = await request(app)
    .post('/orders')
    .send({ items: [{ productId: 'p1', qty: 2 }] });

  expect(response.status).toBe(201);
  expect(response.body.orderId).toBeDefined();
});

Test the unhappy paths equally: 400 for invalid input, 401 for missing auth, 404 for missing resource, 409 for conflicts.

Implement contract testing between services

Contract testing decouples service teams without sacrificing confidence. The consumer defines what it expects; the provider proves it can deliver.

Pact workflow:

1. Consumer writes a pact test defining the expected request/response shape
2. Running the consumer test generates a pact file (JSON contract)
3. Provider runs a pact verification test against that contract
4. Both upload results to a Pact Broker - can-i-deploy gates deployment

Key rules:

• The consumer owns the contract, not the provider
• Contracts test shape and semantics, not business logic
• Never test every field - only what the consumer actually uses

Measure and improve test quality - not just coverage

Line coverage is a floor, not a ceiling. Use these signals instead:

1. Mutation score - Run a mutation testing tool (Stryker, PITest). If removing a > 0 check doesn't kill any test, your tests aren't asserting enough.
2. Test failure rate - Track which tests fail in CI over time. Tests that never fail on a production bug aren't exercising real risk.
3. Test change frequency - Tests that change every time production code changes are testing implementation, not behavior.
4. Time to red - How quickly does the suite tell you when something breaks? Optimize for signal speed, not raw pass/fail.

Handle flaky tests systematically

Never re-run a flaky test and call it fixed. Follow this protocol:

1. Quarantine immediately - Move the flaky test to a separate suite that runs but doesn't block CI. Don't delete it - you'll lose the signal.
2. Diagnose the root cause - Common causes:
   • Shared mutable state between tests (missing cleanup)
   • Time-dependent assertions (Date.now(), setTimeout)
   • Race conditions in async tests (missing await)
   • External service calls that should be stubbed
   • Test order dependency
3. Fix the root cause - If time-dependent: freeze time with a clock fake. If shared state: isolate in beforeEach/afterEach. If async: await properly.
4. Un-quarantine and monitor - After the fix, restore to main suite and watch for a week of clean runs before declaring victory.

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Anti-patterns

Anti-pattern	Problem	What to do instead
Testing the framework	expect(orm.save).toHaveBeenCalled() tests that the ORM is wired, not that data was saved	Assert the actual state after the operation
Snapshot testing everything	Snapshot tests fail on any UI change, creating noise and review fatigue	Use snapshots only for serialized output you rarely change (e.g., generated JSON schema)
100% coverage target	Creates tests that execute code without asserting anything meaningful	Set mutation score targets instead; aim for critical-path coverage
Giant test setup	Hundreds of lines of arrange code obscures what's actually being tested	Use builder/factory patterns; set only the fields that matter to the specific test
Mocking what you don't own	Mocking third-party libraries breaks on upgrades and doesn't test actual integration	Write a thin adapter you own, then mock your adapter
Skipping the testing pyramid for greenfield	Starting with e2e tests "because they test everything" leads to slow, brittle suites	Build bottom-up: unit tests first, integration second, e2e last

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References

For detailed content on specific topics, read the relevant file from references/:

• references/test-patterns.md - Common testing patterns: builders, fakes, parameterized tests, and when to use each

Only load a references file if the current task requires deep detail on that topic.

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Related skills

When this skill is activated, check if the following companion skills are installed. For any that are missing, mention them to the user and offer to install before proceeding with the task. Example: "I notice you don't have [skill] installed yet - it pairs well with this skill. Want me to install it?"

• jest-vitest - Writing unit tests with Jest or Vitest, implementing mocking strategies, configuring test...
• cypress-testing - Writing Cypress e2e or component tests, creating custom commands, intercepting network...
• playwright-testing - Writing Playwright tests, implementing visual regression, testing APIs, or automating browser interactions.
• clean-code - Reviewing, writing, or refactoring code for cleanliness and maintainability following Robert C.

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