Parameterizing Test Cases in Python
Parameterization is a powerful technique in QA automation that allows you to write a single test function and run it with multiple sets of input data. By parameterizing your test cases, you avoid writing repetitive code for each scenario, making your test suite more maintainable and scalable. When you use parameterization, you can easily add new test scenarios by updating your data, rather than duplicating entire test functions.
def add(a, b):
return a + b
def test_addition(input1, input2, expected):
result = add(input1, input2)
assert result == expected, f"Expected {expected}, got {result}"
# Example usage:
test_addition(2, 3, 5)
test_addition(-1, 1, 0)
To make your tests even more efficient, you can store multiple sets of test data and loop through them, calling your parameterized test function for each case. This approach keeps your code clean and makes it easy to update or expand your test coverage. By organizing your test cases in a list or similar structure, you can iterate through each scenario and automatically run the same checks without manual duplication.
test_cases = [
(2, 3, 5),
(0, 0, 0),
(-1, 1, 0),
(100, 200, 300)
]
for input1, input2, expected in test_cases:
test_addition(input1, input2, expected)
1. What is the main advantage of parameterizing test cases?
2. How can you store multiple sets of test parameters in Python?
3. Fill in the blank: for input, expected in test_cases: _____
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Can you explain how parameterization works in popular testing frameworks like pytest?
What are the benefits of using parameterized tests in large projects?
Can you show how to handle more complex test data with parameterization?
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Parameterization is a powerful technique in QA automation that allows you to write a single test function and run it with multiple sets of input data. By parameterizing your test cases, you avoid writing repetitive code for each scenario, making your test suite more maintainable and scalable. When you use parameterization, you can easily add new test scenarios by updating your data, rather than duplicating entire test functions.
def add(a, b):
return a + b
def test_addition(input1, input2, expected):
result = add(input1, input2)
assert result == expected, f"Expected {expected}, got {result}"
# Example usage:
test_addition(2, 3, 5)
test_addition(-1, 1, 0)
To make your tests even more efficient, you can store multiple sets of test data and loop through them, calling your parameterized test function for each case. This approach keeps your code clean and makes it easy to update or expand your test coverage. By organizing your test cases in a list or similar structure, you can iterate through each scenario and automatically run the same checks without manual duplication.
test_cases = [
(2, 3, 5),
(0, 0, 0),
(-1, 1, 0),
(100, 200, 300)
]
for input1, input2, expected in test_cases:
test_addition(input1, input2, expected)
1. What is the main advantage of parameterizing test cases?
2. How can you store multiple sets of test parameters in Python?
3. Fill in the blank: for input, expected in test_cases: _____
Дякуємо за ваш відгук!
