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JavaScript testing necessitates a methodical approach to handle complex scenarios. I've developed advanced testing strategies throughout my career that significantly improve code reliability. Let's explore seven testing patterns that address challenging edge cases.
Error Boundaries Testing
Error boundaries in React provide a safety net for component failures. Testing them requires deliberately creating failure conditions.
// Error boundary component
class ErrorBoundary extends React.Component {
constructor(props) {
super(props);
this.state = { hasError: false, error: null };
}
static getDerivedStateFromError(error) {
return { hasError: true, error };
}
render() {
if (this.state.hasError) {
return <h2>Something went wrong: {this.state.error.message}</h2>;
}
return this.props.children;
}
}
// Test for error boundary
test('ErrorBoundary catches rendering errors', () => {
const ProblemComponent = () => {
throw new Error('Test error');
return <div>Never renders</div>;
};
const { getByText } = render(
<ErrorBoundary>
<ProblemComponent />
</ErrorBoundary>
);
expect(getByText(/Test error/)).toBeInTheDocument();
});
I find testing error boundaries particularly valuable for applications with complex rendering logic. In a project managing financial data, error boundaries prevented crashed interfaces when unexpected data formats appeared.
Race Condition Detection
Race conditions are notoriously difficult to catch. Controlled timing in tests helps expose these issues.
test('handles concurrent requests correctly', async () => {
// Setup a delayed resolver
const createDelayedPromise = (value, delay) => {
return new Promise(resolve => {
setTimeout(() => resolve(value), delay);
});
};
// Mock API with controlled timing
const api = {
getData: jest.fn()
.mockImplementationOnce(() => createDelayedPromise('response1', 200))
.mockImplementationOnce(() => createDelayedPromise('response2', 100))
};
// Function to test
const fetchDataTwice = async () => {
const request1 = api.getData();
const request2 = api.getData();
const result1 = await request1;
const result2 = await request2;
return [result1, result2];
};
const results = await fetchDataTwice();
// Verify results are as expected regardless of timing
expect(results).toEqual(['response1', 'response2']);
});
This pattern helped me identify a critical bug in a payment processing system where overlapping API calls created duplicate transactions.
Locale-Sensitive Testing
Applications used globally must handle internationalization correctly. Testing across locales prevents regional bugs.
describe('internationalization handling', () => {
const originalLocale = Intl.NumberFormat().resolvedOptions().locale;
afterEach(() => {
// Reset locale after each test
mockBrowserLocale(originalLocale);
});
test('formats currency according to locale', () => {
// Mock German locale
mockBrowserLocale('de-DE');
const formatCurrency = (amount) => {
return new Intl.NumberFormat(navigator.language, {
style: 'currency',
currency: 'EUR'
}).format(amount);
};
expect(formatCurrency(1234.56)).toBe('1.234,56 €');
// Mock US locale
mockBrowserLocale('en-US');
expect(formatCurrency(1234.56)).toBe('€1,234.56');
});
// Helper to mock browser locale
function mockBrowserLocale(locale) {
const originalNavigator = global.navigator;
global.navigator = {
...originalNavigator,
language: locale
};
}
});
While developing an e-commerce platform, this approach helped us identify critical sorting issues that appeared only in specific locales.
Timeout Testing
Reliable applications must handle network delays and timeouts gracefully. Timeout testing simulates these conditions.
test('recovers from API timeout', async () => {
jest.useFakeTimers();
// Mock a function that times out
const fetchWithTimeout = (timeout = 5000) => {
return new Promise((resolve, reject) => {
const timeoutId = setTimeout(() => {
reject(new Error('Request timed out'));
}, timeout);
fetch('/api/data')
.then(response => {
clearTimeout(timeoutId);
resolve(response);
})
.catch(error => {
clearTimeout(timeoutId);
reject(error);
});
});
};
// Mock global fetch
global.fetch = jest.fn(() => new Promise(resolve => {
// This promise never resolves, simulating a hanging request
}));
const fetchPromise = fetchWithTimeout(1000);
// Fast-forward time to trigger timeout
jest.advanceTimersByTime(1500);
await expect(fetchPromise).rejects.toThrow('Request timed out');
jest.useRealTimers();
});
This pattern proved vital in a real-time chat application I worked on, ensuring message delivery was reliable even on unstable connections.
State Machine Testing
Applications with complex workflows benefit from state machine testing. This ensures all possible transitions behave correctly.
describe('Order state machine', () => {
// Define a simple order state machine
const createOrderMachine = (initialState = 'pending') => {
const states = {
pending: ['processing', 'canceled'],
processing: ['shipped', 'canceled'],
shipped: ['delivered', 'returned'],
delivered: ['returned'],
returned: [],
canceled: []
};
let currentState = initialState;
return {
getState: () => currentState,
transition: (newState) => {
if (!states[currentState].includes(newState)) {
throw new Error(`Invalid transition from ${currentState} to ${newState}`);
}
currentState = newState;
return currentState;
}
};
};
test('allows valid transitions', () => {
const order = createOrderMachine();
expect(order.getState()).toBe('pending');
expect(order.transition('processing')).toBe('processing');
expect(order.transition('shipped')).toBe('shipped');
expect(order.transition('delivered')).toBe('delivered');
});
test('rejects invalid transitions', () => {
const order = createOrderMachine();
// Can't go from pending directly to shipped
expect(() => order.transition('shipped')).toThrow('Invalid transition');
// Can't go from canceled to any other state
order.transition('canceled');
expect(() => order.transition('processing')).toThrow('Invalid transition');
});
test('handles all possible valid paths', () => {
// Testing all possible valid paths
const validPaths = [
['pending', 'processing', 'shipped', 'delivered'],
['pending', 'processing', 'shipped', 'returned'],
['pending', 'processing', 'canceled'],
['pending', 'canceled']
];
validPaths.forEach(path => {
const order = createOrderMachine();
let currentState = path[0];
// Skip the first state (it's the initial state)
for (let i = 1; i < path.length; i++) {
expect(() => order.transition(path[i])).not.toThrow();
currentState = path[i];
expect(order.getState()).toBe(currentState);
}
});
});
});
I implemented this in an order management system, catching numerous edge cases where orders could reach inconsistent states.
Partial Failure Testing
Systems must handle partial failures gracefully. Testing these scenarios prepares applications for real-world issues.
test('handles partial API response', async () => {
// Mock an API that returns incomplete data
const fetchUserData = async (userId) => {
const response = await fetch(`/api/users/${userId}`);
return response.json();
};
// Mock implementation
global.fetch = jest.fn().mockImplementation(() =>
Promise.resolve({
json: () => Promise.resolve({
id: 123,
// Missing expected fields like name, email
lastLogin: '2023-01-01'
})
})
);
// Function that uses the API data
const displayUserProfile = async (userId) => {
try {
const data = await fetchUserData(userId);
return {
displayName: data.name || 'Unknown User',
contact: data.email || 'No email provided',
id: data.id,
lastSeen: data.lastLogin ? new Date(data.lastLogin) : 'Never'
};
} catch (error) {
return { error: true, message: 'Failed to load user data' };
}
};
const profile = await displayUserProfile(123);
// Verify the function handles missing data gracefully
expect(profile).toEqual({
displayName: 'Unknown User',
contact: 'No email provided',
id: 123,
lastSeen: new Date('2023-01-01')
});
});
This testing pattern helped us maintain service quality during a major API migration where backend services occasionally returned incomplete data.
Boundary Value Testing
Testing boundary conditions exposes many bugs. This approach focuses on edge cases in inputs and outputs.
describe('array processing function', () => {
// Function that processes arrays with a size limit
const processArray = (arr, maxSize = 1000) => {
if (!Array.isArray(arr)) {
throw new TypeError('Input must be an array');
}
if (arr.length > maxSize) {
throw new RangeError(`Array exceeds maximum size of ${maxSize}`);
}
if (arr.length === 0) {
return { result: [], status: 'empty' };
}
const result = arr.map(item => {
if (typeof item !== 'number') {
return 0;
}
return item * 2;
});
return { result, status: 'success' };
};
test('handles normal case', () => {
expect(processArray([1, 2, 3])).toEqual({
result: [2, 4, 6],
status: 'success'
});
});
test('handles empty array', () => {
expect(processArray([])).toEqual({
result: [],
status: 'empty'
});
});
test('handles maximum size array', () => {
const maxArray = Array(1000).fill(1);
expect(processArray(maxArray).result.length).toBe(1000);
});
test('rejects oversized array', () => {
const oversizedArray = Array(1001).fill(1);
expect(() => processArray(oversizedArray)).toThrow(RangeError);
});
test('handles non-numeric values', () => {
expect(processArray([1, 'two', 3, null, undefined])).toEqual({
result: [2, 0, 6, 0, 0],
status: 'success'
});
});
test('rejects non-array input', () => {
expect(() => processArray('not an array')).toThrow(TypeError);
expect(() => processArray(123)).toThrow(TypeError);
expect(() => processArray(null)).toThrow(TypeError);
expect(() => processArray(undefined)).toThrow(TypeError);
});
test('handles edge case values', () => {
expect(processArray([Number.MAX_SAFE_INTEGER])).toEqual({
result: [Number.MAX_SAFE_INTEGER * 2],
status: 'success'
});
expect(processArray([Number.MIN_SAFE_INTEGER])).toEqual({
result: [Number.MIN_SAFE_INTEGER * 2],
status: 'success'
});
expect(processArray([Infinity, -Infinity, NaN])).toEqual({
result: [Infinity, -Infinity, NaN],
status: 'success'
});
});
});
This approach helped me catch unexpected issues in a data processing library that failed on certain numerical edge cases.
Testing edge cases requires creative thinking about potential failure scenarios. These patterns have saved my teams countless hours of debugging and prevented production issues.
The most successful testing strategies combine these patterns into a comprehensive approach. I've learned that investing time in thorough testing pays dividends through increased reliability and reduced maintenance costs.
I recommend starting with boundary value testing and gradually adding other patterns as your application complexity increases. Remember that tests should evolve alongside your code to maintain quality throughout the development lifecycle.
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