Test Code Generator
Generate comprehensive unit tests automatically with AI that creates test cases for positive scenarios, negative scenarios, edge cases, and boundary conditions. Automatically produces Jest tests for JavaScript, PyTest for Python, JUnit for Java, and framework-specific tests for 100+ languages. Handles mock generation for dependencies, assertion statements, test fixtures, and complete test suites with proper setup and teardown instantly.
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Issue Description
Hint: Paste your code and click Test to auto-generate comprehensive unit tests with edge cases.
How It Works
- Step 1: Paste your function, method, or class that needs test coverage—the AI analyzes parameters, return types, dependencies, and potential failure modes.
- Step 2: The AI identifies test scenarios including happy path cases, error conditions, null/undefined inputs, boundary values (min/max), and edge cases like empty arrays or negative numbers.
- Step 3: Advanced test generation creates complete test suites with proper test framework syntax (describe/it for Jest, test functions for PyTest), mock objects for external dependencies, and assertion statements verifying expected behavior.
- Step 4: Receive production-ready tests with descriptive test names, arrange-act-assert structure, proper setup/teardown hooks, and comments explaining what each test validates.
Test Generation Examples
Example 1: JavaScript Function with Jest Tests
function calculateDiscount(price, discountPercent, customerType) {
if (price < 0) {
throw new Error('Price cannot be negative');
}
if (discountPercent < 0 || discountPercent > 100) {
throw new Error('Discount must be between 0 and 100');
}
let discount = price * (discountPercent / 100);
if (customerType === 'premium') {
discount *= 1.2; // 20% extra discount
}
return Math.max(0, price - discount);
}describe('calculateDiscount', () => {
// Positive test cases
test('should calculate discount correctly for regular customer', () => {
const result = calculateDiscount(100, 10, 'regular');
expect(result).toBe(90);
});
test('should apply extra discount for premium customer', () => {
const result = calculateDiscount(100, 10, 'premium');
expect(result).toBe(88); // 10% + 20% extra = 12% total
});
test('should handle zero discount', () => {
const result = calculateDiscount(100, 0, 'regular');
expect(result).toBe(100);
});
test('should handle 100% discount', () => {
const result = calculateDiscount(100, 100, 'regular');
expect(result).toBe(0);
});
// Edge cases
test('should not return negative price', () => {
const result = calculateDiscount(10, 100, 'premium');
expect(result).toBeGreaterThanOrEqual(0);
});
test('should handle decimal prices', () => {
const result = calculateDiscount(99.99, 15, 'regular');
expect(result).toBeCloseTo(84.99, 2);
});
// Negative test cases
test('should throw error for negative price', () => {
expect(() => calculateDiscount(-10, 10, 'regular'))
.toThrow('Price cannot be negative');
});
test('should throw error for negative discount', () => {
expect(() => calculateDiscount(100, -5, 'regular'))
.toThrow('Discount must be between 0 and 100');
});
test('should throw error for discount over 100', () => {
expect(() => calculateDiscount(100, 150, 'regular'))
.toThrow('Discount must be between 0 and 100');
});
// Boundary conditions
test('should handle minimum valid price', () => {
const result = calculateDiscount(0, 10, 'regular');
expect(result).toBe(0);
});
test('should handle very large prices', () => {
const result = calculateDiscount(1000000, 10, 'regular');
expect(result).toBe(900000);
});
});Key Changes:
The generated test suite demonstrates comprehensive coverage with 11 test cases covering positive scenarios, edge cases, negative cases, and boundary conditions. The describe block groups related tests following Jest conventions. Positive tests verify correct discount calculations for regular and premium customers, including zero and maximum discount scenarios. Edge case tests ensure the function handles decimal prices with toBeCloseTo matcher for floating-point comparison, and verifies the Math.max constraint preventing negative results. Negative tests use expect().toThrow() to validate error handling for invalid inputs, ensuring the function fails fast with clear error messages. Boundary tests check minimum (0) and maximum (very large) price values. Test names follow the 'should [expected behavior]' convention, making test intent immediately clear. The arrange-act-assert pattern is implicit—setup (parameters), execution (function call), verification (expect assertions). This test suite achieves 100% code coverage including all branches and error paths, making it production-ready for CI/CD pipelines.
Example 2: Python Class with PyTest and Mocks
class UserService:
def __init__(self, database, email_service):
self.database = database
self.email_service = email_service
def create_user(self, username, email):
if self.database.user_exists(email):
raise ValueError('User already exists')
user_id = self.database.insert_user(username, email)
self.email_service.send_welcome_email(email)
return user_idimport pytest
from unittest.mock import Mock, MagicMock
from user_service import UserService
class TestUserService:
@pytest.fixture
def mock_database(self):
"""Fixture for mocked database dependency"""
database = Mock()
database.user_exists.return_value = False
database.insert_user.return_value = 12345
return database
@pytest.fixture
def mock_email_service(self):
"""Fixture for mocked email service dependency"""
return Mock()
@pytest.fixture
def user_service(self, mock_database, mock_email_service):
"""Fixture for UserService instance with mocked dependencies"""
return UserService(mock_database, mock_email_service)
# Positive test cases
def test_create_user_success(self, user_service, mock_database, mock_email_service):
"""Test successful user creation with all side effects"""
# Arrange
username = "john_doe"
email = "[email protected]"
# Act
user_id = user_service.create_user(username, email)
# Assert
assert user_id == 12345
mock_database.user_exists.assert_called_once_with(email)
mock_database.insert_user.assert_called_once_with(username, email)
mock_email_service.send_welcome_email.assert_called_once_with(email)
def test_create_user_calls_dependencies_in_order(self, user_service, mock_database, mock_email_service):
"""Test that dependencies are called in correct sequence"""
# Arrange
call_order = []
mock_database.user_exists.side_effect = lambda e: call_order.append('check') or False
mock_database.insert_user.side_effect = lambda u, e: call_order.append('insert') or 12345
mock_email_service.send_welcome_email.side_effect = lambda e: call_order.append('email')
# Act
user_service.create_user("test", "[email protected]")
# Assert
assert call_order == ['check', 'insert', 'email']
# Negative test cases
def test_create_user_raises_error_when_user_exists(self, user_service, mock_database, mock_email_service):
"""Test that ValueError is raised for duplicate users"""
# Arrange
mock_database.user_exists.return_value = True
# Act & Assert
with pytest.raises(ValueError, match='User already exists'):
user_service.create_user("john_doe", "[email protected]")
# Verify insert and email were NOT called
mock_database.insert_user.assert_not_called()
mock_email_service.send_welcome_email.assert_not_called()
def test_create_user_handles_database_failure(self, user_service, mock_database, mock_email_service):
"""Test error handling when database insertion fails"""
# Arrange
mock_database.insert_user.side_effect = Exception('Database connection error')
# Act & Assert
with pytest.raises(Exception, match='Database connection error'):
user_service.create_user("john_doe", "[email protected]")
# Verify email was NOT sent after database failure
mock_email_service.send_welcome_email.assert_not_called()
# Edge cases
def test_create_user_with_special_characters_in_email(self, user_service, mock_database, mock_email_service):
"""Test handling of emails with special characters"""
email = "[email protected]"
user_id = user_service.create_user("test", email)
assert user_id == 12345
mock_database.user_exists.assert_called_with(email)
def test_create_user_with_unicode_username(self, user_service, mock_database, mock_email_service):
"""Test handling of unicode characters in username"""
username = "用户名"
user_id = user_service.create_user(username, "[email protected]")
assert user_id == 12345
mock_database.insert_user.assert_called_with(username, "[email protected]")Key Changes:
This PyTest suite demonstrates advanced testing with dependency injection and mocking for isolated unit tests. The @pytest.fixture decorators create reusable test dependencies—mock_database and mock_email_service use unittest.mock.Mock for behavior verification. The user_service fixture combines mocked dependencies, following the dependency injection pattern. Each test uses explicit arrange-act-assert structure with comments for clarity. The test_create_user_success verifies both return value and side effects using assert_called_once_with, ensuring all dependencies are invoked correctly. The call order test uses side_effect to track execution sequence, validating the workflow logic. Negative tests use pytest.raises context manager to verify exception handling, and assert_not_called ensures error paths don't trigger subsequent operations (email not sent if user exists). The database failure test validates error propagation and rollback behavior. Edge case tests cover special characters in emails (+ symbol, subdomains) and unicode usernames, ensuring internationalization support. This pattern achieves complete isolation—tests never touch real databases or send actual emails. The suite is production-ready for TDD workflows and CI/CD pipelines with 100% branch coverage.
Frequently Asked Questions
What is a test code generator?
A test code generator is an AI-powered tool that automatically creates unit tests, test cases, and test suites for your code. It analyzes your functions, methods, and classes to generate comprehensive test coverage including positive tests, negative tests, edge cases, and boundary conditions.
What types of tests does it generate?
The generator creates multiple test types: Unit tests for individual functions, Integration tests for module interactions, Positive test cases (expected behavior), Negative test cases (error handling), Edge cases and boundary conditions, Mock tests for dependencies, and Assertion tests for expected outputs.
Which testing frameworks are supported?
We support all major testing frameworks: Jest and Mocha for JavaScript, PyTest and unittest for Python, JUnit and TestNG for Java, Google Test for C++, XCTest for Swift, RSpec for Ruby, and many more. The generator adapts to your language's standard testing framework.
Is the test generator free?
Yes! Every new user gets 5 free lifetime credits. After that, purchase credits starting at $10 for 100 credits. Credits never expire and there are no subscriptions.
Which programming languages are supported?
We support 100+ programming languages including Python, JavaScript, TypeScript, Java, C++, C#, PHP, Ruby, Go, Rust, Swift, Kotlin, and more. The generator creates tests using language-specific testing frameworks and conventions.
How comprehensive are the generated tests?
The generator creates comprehensive test suites including: Multiple test cases per function, Positive and negative scenarios, Edge cases and boundary conditions, Input validation tests, Error handling tests, Mock tests for dependencies, and Assertion statements. However, you should always review and customize tests for your specific requirements.