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Testing Anti-Patterns

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Testing Anti-Patterns

You are identifying and avoiding common testing anti-patterns. These patterns lead to unreliable tests, false confidence, and maintenance burden.

Critical Anti-Patterns

1. The Liar - Tests That Always Pass

Problem: Test passes even when the code is broken.

// BAD - Always passes because it tests nothing meaningful
it('should process data', () => {
  const result = processData(input);
  expect(result).toBeDefined(); // Too weak
});

// GOOD - Actually verifies behavior
it('should transform input to uppercase', () => {
  const result = processData({ text: 'hello' });
  expect(result.text).toBe('HELLO');
});

Detection: Remove or break the implementation - test should fail.

2. The Giant - Tests Too Large

Problem: Single test covers too many behaviors.

// BAD - Tests multiple things
it('should handle user registration', async () => {
  const user = await register(userData);
  expect(user.id).toBeDefined();
  expect(user.email).toBe(userData.email);
  expect(user.password).toBeUndefined();
  expect(sendEmail).toHaveBeenCalled();
  expect(createProfile).toHaveBeenCalled();
  // ... 20 more assertions
});

// GOOD - Focused tests
it('should create user with provided email', async () => {
  const user = await register(userData);
  expect(user.email).toBe(userData.email);
});

it('should send welcome email on registration', async () => {
  await register(userData);
  expect(sendEmail).toHaveBeenCalledWith(
    expect.objectContaining({ type: 'welcome' })
  );
});

Fix: One test, one logical assertion concept.

3. The Inspector - Testing Implementation Details

Problem: Test breaks when implementation changes, even if behavior is correct.

// BAD - Tests internal implementation
it('should use QuickSort for sorting', () => {
  const sorter = new Sorter();
  const spy = jest.spyOn(sorter, '_quickSort');
  sorter.sort([3, 1, 2]);
  expect(spy).toHaveBeenCalled();
});

// GOOD - Tests behavior/output
it('should return sorted array', () => {
  const sorter = new Sorter();
  expect(sorter.sort([3, 1, 2])).toEqual([1, 2, 3]);
});

Fix: Test what the code does, not how it does it.

4. The Mockery - Over-Mocking

Problem: Too many mocks make tests meaningless.

// BAD - Everything is mocked, test proves nothing
it('should calculate price', () => {
  const mockProduct = { getPrice: jest.fn().mockReturnValue(100) };
  const mockDiscount = { apply: jest.fn().mockReturnValue(80) };
  const mockTax = { calculate: jest.fn().mockReturnValue(8) };

  const total = calculateTotal(mockProduct, mockDiscount, mockTax);
  expect(total).toBe(88); // Just testing mock arithmetic
});

// GOOD - Use real objects where feasible
it('should apply 20% discount to price', () => {
  const product = new Product({ price: 100 });
  const discount = new PercentageDiscount(20);

  const total = calculateTotal(product, discount);
  expect(total).toBe(80);
});

Fix: Only mock external dependencies and side effects.

5. The Flaky Test - Random Failures

Problem: Test sometimes passes, sometimes fails.

Common causes:

  • Time-dependent logic
  • Race conditions in async code
  • Shared mutable state
  • External dependencies
// BAD - Depends on current time
it('should show recent items', () => {
  const item = { createdAt: new Date() };
  expect(isRecent(item)).toBe(true);
});

// GOOD - Control the time
it('should show items from last 24 hours', () => {
  const now = new Date('2024-01-15T12:00:00Z');
  jest.setSystemTime(now);

  const recent = { createdAt: new Date('2024-01-15T00:00:00Z') };
  const old = { createdAt: new Date('2024-01-13T00:00:00Z') };

  expect(isRecent(recent)).toBe(true);
  expect(isRecent(old)).toBe(false);
});

6. The Slow Poke - Unnecessarily Slow Tests

Problem: Tests take too long to run.

// BAD - Real network call
it('should fetch user data', async () => {
  const response = await fetch('https://api.example.com/users/1');
  const user = await response.json();
  expect(user.name).toBeDefined();
});

// GOOD - Mocked network
it('should parse user response', async () => {
  mockFetch.mockResolvedValue({
    json: () => Promise.resolve({ id: 1, name: 'Test User' })
  });

  const user = await fetchUser(1);
  expect(user.name).toBe('Test User');
});

Target: Unit tests < 100ms, Integration tests < 1s.

7. The Chain Gang - Test Dependency

Problem: Tests depend on other tests running first.

// BAD - Tests must run in order
describe('User operations', () => {
  let userId;

  it('should create user', () => {
    userId = createUser(); // Sets state for next test
    expect(userId).toBeDefined();
  });

  it('should update user', () => {
    updateUser(userId, newData); // Depends on previous test
    expect(getUser(userId).name).toBe(newData.name);
  });
});

// GOOD - Each test is independent
describe('User operations', () => {
  it('should create user', () => {
    const userId = createUser();
    expect(userId).toBeDefined();
  });

  it('should update user', () => {
    const userId = createUser(); // Creates its own user
    updateUser(userId, newData);
    expect(getUser(userId).name).toBe(newData.name);
  });
});

8. The Secret Catcher - Hidden Test Logic

Problem: Test logic is hidden in helpers or setup.

// BAD - Assertions hidden in helper
function assertValidUser(user) {
  expect(user.id).toBeDefined();
  expect(user.email).toMatch(/@/);
  expect(user.createdAt).toBeInstanceOf(Date);
  // Many more hidden assertions
}

it('should create valid user', () => {
  const user = createUser(data);
  assertValidUser(user); // What is actually being tested?
});

// GOOD - Explicit assertions
it('should create user with email', () => {
  const user = createUser(data);
  expect(user.email).toBe(data.email);
});

Anti-Pattern Detection Checklist

When reviewing tests, watch for:

  • Tests without meaningful assertions
  • Tests with more than 5-7 assertions
  • Tests that mock everything
  • Tests that access private methods/properties
  • Tests with sleep/wait calls
  • Tests that depend on test execution order
  • Tests with complex setup that obscures intent

Refactoring Strategies

  1. Too many assertions → Split into multiple tests
  2. Over-mocking → Use real implementations or fakes
  3. Flaky tests → Control time, mock external calls
  4. Slow tests → Mock I/O, parallelize independent tests
  5. Hidden logic → Inline or clearly name helpers

When to Delete Tests

Tests that:

  • Always pass regardless of implementation
  • Test third-party library behavior
  • Are permanently flaky without fix
  • Duplicate other tests exactly
  • Test deprecated code

Source

git clone https://github.com/rohitg00/skillkit/blob/main/packages/core/src/methodology/packs/testing/anti-patterns/SKILL.mdView on GitHub

Overview

Testing anti-patterns undermine reliability and maintenance. This skill helps you recognize patterns like The Liar, The Giant, The Inspector, The Mockery, and The Flaky Test, and replaces them with focused, deterministic tests that reflect actual behavior.

How This Skill Works

The approach labels each anti-pattern, audits tests for the telltale signs, and prescribes concrete fixes. You learn to verify observable behavior instead of internal details, keep tests small and purposeful, mock only external dependencies, and stabilize timing and shared state to prevent flakiness.

When to Use It

  • When a test passes but the code is broken (The Liar).
  • When a single test covers many behaviors (The Giant).
  • When tests depend on internal implementation details (The Inspector).
  • When tests rely on extensive mocks over real objects (The Mockery).
  • When test results vary between runs (The Flaky Test).

Quick Start

  1. Step 1: Scan your test suite for The Liar, The Giant, The Inspector, The Mockery, and The Flaky Test.
  2. Step 2: Refactor to focus on observable behavior with smaller, independent assertions.
  3. Step 3: Introduce deterministic controls (fake timers, fixed data) and minimize external mocks.

Best Practices

  • Write focused tests that validate a single behavior or outcome.
  • Test observable behavior, not internal implementation details.
  • Mock external dependencies sparingly; prefer real objects when safe.
  • Split large test cases into smaller, logically independent tests.
  • Stabilize tests by controlling time, RNG, and shared state to reduce flakiness.

Example Use Cases

  • Liar: BAD—test passes by only checking result is defined; GOOD—assert the actual transformation, e.g., text uppercased.
  • Giant: BAD—one test covers registration and multiple assertions; GOOD—split into focused tests like email creation and welcome email sent.
  • Inspector: BAD—tests peek at private methods; GOOD—verify final sorted output.
  • Mockery: BAD—tests rely on heavy mocks of all collaborators; GOOD—use real Product and Discount objects where feasible.
  • Flaky: BAD—test depends on current time; GOOD—control time with fake timers or fixed timestamps.

Frequently Asked Questions

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