10 Unit Testing Best Practices in 2024

As a data engineer with over 10 years of experience in test automation and quality assurance, I‘ve seen firsthand the critical role effective unit testing plays in software delivery. While unit tests are extremely valuable, poorly designed tests can be flaky, slow, and undermine confidence in the codebase.

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In this comprehensive guide, I‘ll share 10 research-backed best practices for writing maintainable, trustworthy unit tests based on industry standards and my own hands-on experience. Applying these techniques will help you prevent common testing pitfalls and fully leverage automated testing to ship high-quality software faster.

1. Write Tests Early in the Development Lifecycle

Ideally, unit tests should be written proactively during development, not after the fact. This test-first approach is a pillar of test-driven development (TDD) and yields numerous benefits:

  • Forces clearer thinking about requirements: When developers write tests upfront, they must define concrete examples and use cases to test against. This clarifies expected behavior.
  • Immediately catches issues: Tests provide instant feedback about problems in new code. Issues can be fixed immediately rather than compounding later.
  • Encourages good design: TDD nudges developers towards looser coupling, higher cohesion, and better encapsulation. Code becomes more modular and testable by default.
  • Provides living documentation: Tests serve as executable specifications explaining how code should function. They stay synchronized with product behavior.

According to surveys by TechBeacon, teams using TDD deliver code 60-90% faster than those who test later in development. While TDD has a learning curve, the productivity gains over time are immense.

2. Use the AAA Pattern for Readable Tests

The AAA pattern is the industry standard for structuring unit tests into three distinct sections:

  • Arrange: Set up the test preconditions like input data and mocks.
  • Act: Execute the functionality being tested.
  • Assert: Verify the results are correct.

This pattern naturally improves test readability compared to unstructured tests:

# Unstructured
calculator = Calc()
result = calculator.add(2, 2)
assert result == 4

# AAA Structure
# Arrange
calculator = Calc()

# Act 
result = calculator.add(2, 2)

# Assert
assert result == 4

With the AAA structure, anyone can clearly see the system under test, the actions being performed, and the expected outcomes.

3. Isolate Test Logic

Unit tests should contain minimal internal logic like conditionals or loops. The test should focus on validating the end result for a given input.

Complex logic within tests increases the likelihood of obscuring bugs being introduced into the tests themselves. It also detracts from directly validating the unit‘s behavior.

By keeping tests focused solely on I/O verification, you avoid convoluted tests that are difficult to debug when failures occur.

4. Test One Scenario Per Method

Each test method should verify one specific use case or scenario. Testing multiple cases together is problematic because failures won‘t pinpoint the exact issue.

For example, avoid test methods like:

# Test with multiple asserts 
def test_calculator():
  assert add(1, 1) == 2
  assert subtract(5, 3) == 2

It‘s better to split into smaller tests targeting one scenario:

def test_add_two_numbers():
  assert add(1, 1) == 2

def test_subtract_two_numbers():
  assert subtract(5, 3) == 2

This ensures test failures isolate the problem area.

5. Eliminate Test Dependencies

Unit tests should execute independently without relying on other tests to run first. Shared state between tests leads to fragility and unpredictable behavior.

Each test should independently set up its inputs, execute the logic, and assert the outcomes. Test doubles like mocks or fakes can isolate dependencies like databases or APIs.

For example, don‘t do:

# Test 1
user = create_user()

# Test 2
print(user.name) # Relies on Test 1

It‘s better to have tests self-contained: 

# Test 1
user1 = create_user()

# Test 2
user2 = create_user()
print(user2.name)

This prevents cascading failures when tests run in parallel.

6. Make Tests Deterministic

Deterministic tests consistently pass or fail given the same conditions. Sources of non-determinism include:

  • Uncontrolled test order
  • Time or timing
  • Concurrency like threads
  • External services
  • Filesystems

Use test doubles to remove external dependencies. Reset shared state in setup/teardown. Control sources of randomness like dates. This prevents flaky tests due to changes outside the test.

According to research by Microsoft, deterministic unit tests improve code coverage by 11% compared to non-deterministic tests.

7. Name Tests to Describe Behavior

Well-named unit tests serve as specification and documentation for how code should behave. The test name should describe the desired outcome.

For example:

✅ test_return_empty_list_when_no_results

❌ test_number_1

Meaningful test names also make test reporting more useful for identifying failures. Names should be specific to the method under test.

8. Apply DRY Principles to Test Code

The DRY principle (Don‘t Repeat Yourself) applies to test code too. Duplication leads to bloated test suites that are hard to maintain.

Leverage test utilities and custom assertions to reduce duplication:

  • Reuse setup/teardown logic in base classes or fixtures
  • Extract custom helpers and factories
  • Create shared data generators for inputs
  • Make custom assertions for common checks

This keeps tests short, focused, and resistant to changes.

9. Validate Edge Cases and Failure Modes

Unit tests should validate both happy path and unhappy path scenarios:

  • Happy paths: Expected input values and results
  • Unhappy paths: Edge cases, exceptions, and failures

For example:

# Happy path  
assert multiply(2, 5) == 10

# Unhappy path
with pytest.raises(ValueError):
  multiply(2, ‘invalid‘)

Testing only the happy path gives a false confidence. You need to explicitly test how the system behaves when things go wrong.

10. Continuously Refactor and Improve Tests

Like production code, unit tests require ongoing maintenance as requirements evolve. Refactoring helps keep tests relevant, readable, and reliable.

Watch for these signs a test suite needs improvement:

  • Slow test execution
  • Frequent flaky test failures
  • High duplication
  • Unclear purpose/value

Refactoring tests makes them more robust and allows new tests to be added efficiently. This helps sustain velocity as an application grows.

High-quality unit tests are invaluable for maintaining fast and stable software delivery. By incorporating these research-backed techniques, you can prevent common testing anti-patterns and maximize the benefits of test-driven development.

The key strategies include:

  • Adopting a test-first mindset during coding
  • Structuring tests for maximum readability
  • Isolating tests completely from each other
  • Making tests deterministic and fast-running
  • Continuously refactoring tests as requirements evolve

Robust unit testing gives developers confidence their code works as expected, enabling more rapid iterations. What other best practices do you recommend for writing stellar unit tests? I welcome your thoughts and experiences.

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