Using Loops and Conditions to Control Test Flow

 Using loops and conditions to control test flow is a common practice in automated testing (e.g., unit tests, integration tests, or UI tests). It allows you to dynamically repeat tests, skip certain scenarios, or make decisions during test execution.


Here's a breakdown of how loops and conditions can be used effectively in test control:


🔁 Using Loops in Test Flow

Loops are helpful for repeating tests with different inputs or retrying failed scenarios.


Example 1: Loop Through Test Data

python

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test_data = [("user1", "pass1"), ("user2", "pass2"), ("admin", "admin123")]


for username, password in test_data:

    result = login(username, password)

    assert result == "Success"

Example 2: Retry on Failure

python

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MAX_RETRIES = 3

for attempt in range(MAX_RETRIES):

    result = run_flaky_test()

    if result == "Pass":

        break

else:

    raise AssertionError("Test failed after 3 retries")

❓ Using Conditions in Test Flow

Conditional logic lets you control which tests run based on the situation.


Example 1: Skip Based on Environment

python

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import sys


if sys.platform == "win32":

    print("Skipping test on Windows")

else:

    run_linux_specific_test()

Example 2: Assert Only If Feature is Enabled

python

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def test_feature_x():

    if not is_feature_enabled("feature_x"):

        print("Feature X not enabled, skipping test")

        return


    result = use_feature_x()

    assert result == "Expected Output"

🧪 Combining Loops and Conditions

python

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test_cases = [

    {"input": 5, "expected": 25, "enabled": True},

    {"input": -3, "expected": 9, "enabled": False},  # Skipped

]


for case in test_cases:

    if not case["enabled"]:

        continue  # Skip this test


    output = square_function(case["input"])

    assert output == case["expected"]

✅ Best Practices

Use data-driven testing (looping through input sets) to reduce redundant code.


Use conditions to skip or branch test logic based on config, environment, or test flags.


Be careful not to hide failures—only retry if you can justify it (e.g., flaky tests).


Combine with logging to track which tests were skipped or retried.

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