Why testing prevents costly failures in Testing Fundamentals - Automation Benefits in Action

Verify that a function correctly calculates the total price including tax

Preconditions (3)

Step 1: Call calculate_total_price with price=100.0 and tax_rate=0.1

Step 2: Call calculate_total_price with price=50.0 and tax_rate=0.2

Step 3: Call calculate_total_price with price=0 and tax_rate=0.15

✅ Expected Result: The function returns 110.0, 60.0, and 0.0 respectively for the three calls

Automation Requirements - pytest

Assertions Needed:

Assert that calculate_total_price(100.0, 0.1) == 110.0

Assert that calculate_total_price(50.0, 0.2) == 60.0

Assert that calculate_total_price(0, 0.15) == 0.0

Best Practices:

Use clear and descriptive test function names

Use assert statements for validation

Keep tests independent and simple

Test edge cases such as zero price

Automated Solution

Testing Fundamentals

import pytest

def calculate_total_price(price: float, tax_rate: float) -> float:
    return price + (price * tax_rate)


def test_calculate_total_price():
    assert calculate_total_price(100.0, 0.1) == 110.0
    assert calculate_total_price(50.0, 0.2) == 60.0
    assert calculate_total_price(0, 0.15) == 0.0

if __name__ == "__main__":
    pytest.main()

The calculate_total_price function is defined to add tax to the price.

The test function test_calculate_total_price calls this function with three sets of inputs and uses assert statements to check if the output matches the expected values.

This simple test prevents costly failures by ensuring the calculation logic is correct before using it in real scenarios.

Using pytest allows easy running and reporting of test results.

Common Mistakes - 3 Pitfalls

Not using assert statements to verify output

Testing only one input case

Hardcoding expected results incorrectly

Bonus Challenge

Now add data-driven testing with 3 different sets of price and tax_rate inputs

Show Hint