Code Coverage

Code coverage is just making sure you’ve checked everything, like what it says in the name.

Imagine you’re a car mechanic, and you need to check every part of a car. It’s not enough to just say “I checked the car” — you need to know exactly which parts you’ve inspected. That’s what code coverage is in software testing!

1. What is Code Coverage?

It’s a measure of how much of your code is being tested.
Like a detailed car inspection report, it shows what’s been checked and what hasn’t.
Think of it as a mechanic’s checklist.

Let’s break this down with a real example:

def start_car(key, battery_level):
    if not key:
        return "No key inserted"
    
    if battery_level < 20:
        return "Battery too low"
    
    if check_engine_state():
        warm_up_engine()
        return "Engine started"
    else:
        log_failure()
        return "Engine failed to start"

Types of Coverage (The Different Ways We Check)

A. Statement Coverage:

This is the basic level — have we run each line at least once?
It’s like walking through the car and touching each part.

In our code:

• Did we test with a key?
• Did we test with different battery levels?
• Did the engine start successfully?

B. Branch Coverage:

Did we test each decision path?
It’s like testing what happens when:

• No key → Return error
• Low battery → Return error
• Good conditions → Try to start
• Engine fails → Handle failure

Each if/else needs both true and false tests.

C. Path Coverage:

Testing all possible combinations.
Example paths:

• No key → Error
• Key + Low battery → Error
• Key + Good battery + Engine Success → Started
• Key + Good battery + Engine Fail → Failure

2. How Coverage Helps

Think of these scenarios:

def process_payment(amount, account):
    if amount <= 0:
        return "Invalid amount"
    
    if not check_balance(account):
        return "Insufficient funds"
    
    if process_transaction(amount):
        update_balance(account, amount)
        return "Payment successful"
    else:
        log_error()
        return "Transaction failed"

Coverage would tell us:

• Did we test with negative amounts?
• Did we test with empty accounts?
• Did we test successful transactions?
• Did we test failed transactions?

3. Common Coverage Goals and Why

Different scenarios need different coverage levels:

• Regular Features (70-80%)
  Like checking a used car. Most paths should work, some edge cases acceptable.

• Financial Transactions (90%+)
  Like checking a race car. Almost everything needs to be perfect.

• Critical Systems (100%)
  Like checking an airplane. No room for untested code.

4. The Coverage Trap (Warning Signs)

High Coverage ≠ Good Testing

Example:

def validate_age(age):
    if age >= 18:
        return "Adult"
    return "Minor"

You might test:

• age = 20 → “Adult”
• age = 15 → “Minor”
  100% coverage! But did you test:
• Negative ages?
• Zero?
• Very large numbers?
• Non-number inputs?

Smart Coverage Strategy:

A. Start with Critical Paths

• Login functionality
• Payment processing
• Data saving

B. Add Edge Cases

• Invalid inputs
• System failures
• Resource limitations

C. Use Coverage Tools

• Python: pytest-cov
• JavaScript: Istanbul
• Java: JaCoCo

D. Regular Reviews

• Check coverage reports
• Review uncovered code
• Update tests as code changes

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Remember: Coverage is like a car’s maintenance record — it shows what’s been checked, but not how well. The goal isn’t just to tick boxes, but to ensure everything works reliably!