unit-test-writer

Unit Test Writer Skill

Purpose

This skill provides systematic guidance for writing comprehensive, maintainable unit tests that achieve high coverage and follow best practices across multiple programming languages.

When to Use

• Need to create unit tests for new code
• Want to improve existing test coverage
• Need guidance on test structure and organization
• Writing tests following TDD methodology
• Ensuring proper mocking and fixtures

Unit Testing Principles

Test Structure: Arrange-Act-Assert (AAA)

Every test should follow this clear pattern:

def test_feature_condition_expected():
    """Clear test description."""
    # Arrange: Setup test data and dependencies
    input_data = create_test_data()
    mock_dependency = setup_mock()

    # Act: Execute the code under test
    result = function_under_test(input_data, mock_dependency)

    # Assert: Verify expected outcomes
    assert result.status == "success"
    mock_dependency.method.assert_called_once()

Test Naming Conventions

Python (pytest):

• Pattern: test_<function>_<condition>_<expected_result>
• File: test_<source_file_name>.py
• Examples:
  • test_create_user_valid_data_returns_user
  • test_validate_email_invalid_format_raises_error
  • test_process_data_empty_input_returns_empty_list

JavaScript/TypeScript (Jest):

• Pattern: should <expected behavior> when <condition>
• File: <source_file_name>.test.ts or <source_file_name>.test.js
• Examples:
  • should return user when valid data provided
  • should throw error when email format is invalid
  • should return empty list when input is empty

Coverage Goals

• Overall coverage: ≥ 80%
• Critical business logic: ≥ 90%
• Error handling paths: 100%
• Utility functions: ≥ 85%

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Unit Testing Workflow

1. Analyze Code to Test

Read and understand the source:

# Read the source file
cat src/module/feature.py

# Identify:
# - Functions and classes to test
# - Dependencies and external calls
# - Error conditions and edge cases
# - Input validation requirements