unit-test-generator

Unit Test Generator

Automatically generate comprehensive unit tests for your code.

Core Capabilities

This skill helps you generate high-quality unit tests by:

1. Analyzing source code - Understanding function/class behavior and contracts
2. Identifying test cases - Determining happy paths, edge cases, and error conditions
3. Matching style - Following existing test patterns and conventions in your codebase
4. Generating tests - Creating complete, runnable test code
5. Explaining coverage - Documenting what each test validates

Test Generation Workflow

Step 1: Analyze the Code to Test

Examine the source code to understand:

Function Signature:

• Parameters and their types
• Return type
• Exceptions raised

Function Behavior:

• What the function does
• Preconditions and postconditions
• Side effects (DB writes, API calls, file I/O)
• Dependencies on other code

Example Analysis:

def calculate_discount(price: float, discount_percent: float) -> float:
    """Calculate discounted price.

    Args:
        price: Original price (must be positive)
        discount_percent: Discount percentage (0-100)

    Returns:
        Discounted price

    Raises:
        ValueError: If price is negative or discount is invalid
    """
    if price < 0:
        raise ValueError("Price cannot be negative")
    if not 0 <= discount_percent <= 100:
        raise ValueError("Discount must be between 0 and 100")

    return price * (1 - discount_percent / 100)

Analysis:

• Takes two floats, returns float
• Validates price >= 0
• Validates discount in [0, 100]
• Raises ValueError for invalid inputs
• Pure function (no side effects)

Step 2: Identify Test Cases

Determine all test scenarios using the Comprehensive Coverage approach:

1. Happy Path Tests - Normal, expected usage

• Valid inputs that should succeed
• Typical use cases

2. Edge Case Tests - Boundary conditions

• Zero values
• Maximum/minimum values
• Empty inputs
• Single element inputs