ia-writing-tests

Generic test writing discipline: test quality, real assertions, anti-patterns, and rationalization resistance. Use when writing tests, adding test coverage, or fixing failing tests for any language or framework. Complements language-specific skills.

Writing Tests

Core Principle

Tests prove behavior works. A test that can't fail is worthless. A test that tests mocks instead of real code is theater.

Writing Good Tests

One behavior per test

Each test should verify exactly one thing. If the test name needs "and" in it, split it into two tests.

text

Good:  "creates user with valid email"
Good:  "rejects user with duplicate email"
Bad:   "creates user and sends welcome email and updates counter"

Derive test cases from three sources

Build test coverage from three independent sources and verify every item maps to at least one test:

1. User requirements -- what was requested (spec, issue, conversation)
2. Features implemented -- what the code actually does (scan the diff)
3. Claims in the response -- what is about to be reported to the user as working

Anything in any source with no corresponding test is a coverage gap -- implemented-but-untested features, claimed-but-unverified behavior.

For each source, enumerate user journeys ("As a [role], I want to [action], so that [benefit]") and generate test cases from each, so tests cover user-visible behavior rather than implementation details.

DAMP over DRY in tests

Each test should be independently readable without chasing shared setup through helpers. Duplication in tests is acceptable -- even desirable -- when it makes intent obvious at a glance. Extract shared setup only when it reduces noise without hiding what the test does.

Test pyramid

For API/web projects, aim for ~80% unit / ~15% integration / ~5% E2E; adjust for risk profile (data pipelines may need heavier integration, CLI tools minimal E2E).

• Unit: fast, isolated, one behavior per test, no database/network/filesystem -- the cheap, fast-feedback foundation.
• Integration: verify component boundaries against real dependencies (real test database, wired services, queue producer + consumer) -- catch the wiring bugs mocks hide.
• E2E: critical user paths through the real system only (signup, checkout, core workflow) -- every E2E test must justify its maintenance cost.

Name tests by expected behavior

The test name should describe what happens, not what's being called.

text

Good:  "returns 404 when user does not exist"
Bad:   "test getUserById"
Good:  "sends notification after order is placed"
Bad:   "test processOrder"

Use real objects when practical

Mocks should be a last resort, not a first choice. Every mock is an assumption about behavior that may drift from reality.

Use real objects for	Use mocks/fakes for
Database queries (use test DB)	External HTTP APIs
Internal services and classes	Payment gateways
File system operations (use temp dirs)	Email/SMS delivery
Business logic and transformations	Third-party SDKs with rate limits

Exception: framework-provided test doubles. Framework faking mechanisms (Laravel Queue::fake()/Event::fake(), React test providers, vi.mock for API layers) are idiomatic and maintained alongside the framework -- use them. The rule targets hand-rolled mocks that drift, not framework-blessed utilities.

Tests expose bugs, not the reverse

If a test uncovers broken or buggy behavior, fix the source code -- never adjust the test to match incorrect behavior. A test that passes against a bug is worse than no test at all.

Test edge cases

For every feature, consider:

• Empty input / null / undefined
• Boundary values (0, 1, max, max+1)
• Invalid types (string where number expected)
• Concurrent access (if applicable)
• Error paths (network failure, timeout, permission denied)
• Unicode and special characters in string inputs

Silent failure coverage

Tests must detect silent failures, not just happy paths. For every code path that catches, logs, or short-circuits on error, add an assertion that proves the failure was observable. Hunt targets during test writing:

• Empty catch blocks (try { ... } catch {}) — trigger the error; assert the logger (or equivalent signal) received the original exception.
• Swallowed rejections (.catch(() => []), .catch(() => null)) — trigger the rejection; assert the caller sees a distinguishable signal (specific return value, logged error, re-thrown).
• Converted errors (catch (e) { return defaultValue; }) — assert the return value AND that the error was recorded where an operator can find it.
• Missing async handling — assert a rejected promise inside the function surfaces as a failure, not just an unhandled-rejection warning.
• No rollback around transactional work — assert a mid-transaction failure leaves no partial state (row counts match, queue unchanged).

Assertion pattern: instead of expect(result).toBe(null) (which passes for both "handled gracefully" and "silent drop"), prefer expect(logger.error).toHaveBeenCalledWith(expect.any(DatabaseError)) — make the observable signal part of the contract.

Red-Green-Refactor (When It Applies)

Tests-first answer "what should this do?"; tests-after answer "what does this do?" -- tests written after implementation are biased toward verifying what was built, not what's required. For bug fixes, the failing test first proves the bug exists and the fix works; for new features, the order matters less than the quality.

Bug fixes: prove-it pattern

1. Write a test that reproduces the bug
2. Run it and watch it fail -- confirm it fails for the right reason. A test that fails due to a typo or import error hasn't captured the bug. The failure message should describe the buggy behavior.
3. Apply the fix
4. Run it and watch it pass -- confirm the fix addresses the specific failure AND other tests still pass. A fix that breaks something else isn't a fix.
5. If the test passes immediately without a fix, the test is verifying existing behavior, not the bug. Go back to step 1.

New features: test alongside

Write tests alongside the implementation, not after. By the time the feature is done, tests exist and pass -- whether a test was written 5 minutes before or 5 minutes after the code matters less than whether it exists and is good.

Minimum viability during green phase: When making a test pass, write the simplest code that satisfies it -- not the abstraction that seems "right," not the feature that might be needed next. Refactor only after the test is green.

Anti-Patterns

Extended rationale, fix ladders, and mechanics for the longer items: anti-patterns-extended.md.

Testing mock behavior instead of real behavior

Symptom: Test passes but production breaks. Tests assert that mocks were called correctly, not that the actual system works.

Fix: Replace mocks with real objects for internal code (see "Use real objects when practical").

Test-only methods in production code

Symptom: Methods like reset(), clearState(), setTestMode() that exist only because tests need them.

Fix: If tests need to reset state, the code has a design problem. Refactor to make state explicit and injectable.

Snapshot tests as the only test

Symptom: All tests are snapshots that get bulk-updated whenever anything changes.

Fix: Snapshots catch unintended changes but don't verify correctness. Add behavioral assertions alongside snapshots.

Testing the framework

Symptom: Tests verify that the ORM saves records, the router routes requests, or the framework does what its docs say.

Fix: Trust the framework. Test the project's own logic -- the business rules, transformations, and decisions the code makes.

Incomplete mocks

Symptom: Mock only includes the fields the test author knows about. Downstream code consumes other fields and gets undefined.

Fix: Mock the COMPLETE data structure as it exists in reality -- check what fields the real API/type contains and include everything consumed downstream. Prefer real objects or factory fixtures with all fields populated; if mocking is unavoidable, generate from the real type/schema.

Mocking without understanding

Before mocking any method, ask: (1) What side effects does the real method have? (2) Does this test depend on any of those side effects? (3) Mock at the lowest level that removes the slow/external part -- not higher.

AI-generated test smells

LLM-written tests (including self-written) fail in predictable ways. Before committing, scan every test for these six smells:

• Mock of the system under test — mocking the very function being tested, so the test asserts what the mock returned. Always a mistake. Delete the mock; call the real function.
• Circular assertion — computing the expected value the same way the code computes the actual value (expect(sum(a,b)).toBe(a+b)). The test passes even when both are wrong. Replace with a hand-computed expected value or a known fixture.
• Snapshot of unreviewed output — first-run snapshot committed without reading it. The snapshot enshrines whatever the code happened to emit, bugs included. Hand-write the first snapshot or diff it line by line before accepting.
• Assertion-free exercise — test calls the function, checks nothing, passes because nothing threw. Every test needs at least one expect(...) / assert ... tied to the behavior under test.
• Over-broad matchers — expect(result).toBeTruthy() on a function that returns an object. Passes for {}, true, "anything", all equally. Pin to the specific shape.
• Implementation-echo assertions — expect(repo.save).toHaveBeenCalledTimes(1) when the real contract is "the user exists in the database afterward." Assert on outcomes (row exists, response body contains expected fields), not call counts or internal method invocations.

Persistent test infrastructure state contamination

Symptom: Integration tests fail with row-count multipliers (expected 2 rows, got 8) yet pass on a fresh container -- persistent infrastructure kept state from prior runs. Diagnostic shortcut: a clean integer multiple (2x, 3x, 4x...) between expected and actual means state contamination, not a logic bug -- logic bugs rarely produce uniform multipliers across unrelated assertions.

Fix: Reset infrastructure state between runs -- ephemeral containers, fixture TRUNCATE, or volume teardown (ladder in the reference); never rely on tests "cleaning up after themselves."

Vacuous forall over an empty collection

Symptom: A forall-style assertion (every, all, .iter().all()) passes vacuously -- the factory never attached children, and every such operator returns true over an empty collection.

Fix: Attach a realistic child set and confirm the predicate flips for at least one populated case.

Constructing the object-under-test below the layer that transforms it

Symptom: The fix lives in an upstream transform (parser, normalizer, from_api_response), but the test builds the object via the leaf constructor with the already-correct value -- the transform never runs; green test, broken production.

Fix: Feed the test the raw pre-transform input (API payload, unparsed dict), never the leaf constructor, so the transform under test executes.

Synchronous adapters hide timing-dependent races

Symptom: Parallel requests through a zero-latency mock settle in the same microtask, so a dedup/coalescing guard passes -- under real wire latency, staggered arrivals miss the window and spawn N operations.

Fix: Inject controllable latency (fake timers, staggered deferred resolution); assert the guard holds for arrival-staggered bursts, not just same-tick ones.

Asserting only presence, never absence

Symptom: Payload/serializer tests assert expected fields exist but never that unexpected fields are absent -- a field leaking into a reused builder (CREATE vs UPDATE) passes every existing test.

Fix: Where a field set is a contract, pin absence as well as presence: assert "proof_document_id" not in payload.

When Stuck

Stuck on...	Do this
Don't know how to test	Write the assertion first (desired outcome), then build the test around it
Test too complicated	Simplify the interface being tested
Must mock everything	Code is too coupled -- use dependency injection
Test setup too large	Extract helpers that reduce noise without hiding test intent (see DAMP). Still complex? Simplify the design

Rationalization Table

If about to skip, defer, or argue against writing a test for any reason, STOP and load rationalization-table.md first. Thirteen common excuses with their counter-truths. When arguing against writing a test, the argument is probably lost.

Verify

Before considering tests complete:

• Every new public function/endpoint has at least one test
• Each test has a descriptive name stating expected behavior
• Tests use real objects where possible (mocks only at system boundaries)
• Edge cases covered (empty, null, boundary, error paths)
• Tests assert on outcomes, not implementation details
• Tests are independent -- no shared mutable state between tests. If tests pass individually but fail together, use bisection to find the polluter (run one-by-one in isolation until the offending test is found)
• Tests run fast enough to run frequently (< 30 seconds for unit suite)
• Bug fix tests reproduce the original bug

Integration

This skill covers generic test discipline. For framework-specific patterns, conventions, and tooling:

• Laravel/PHP → ia-php-laravel (PHPUnit, factories, feature/unit split, facade faking, data providers)
• React/TypeScript → ia-react-frontend (Vitest, RTL, component/hook patterns, Playwright E2E, mocking patterns)

When both are active, framework-specific guidance takes precedence for tooling and conventions.