Flaky test management in Testing Fundamentals - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Identifying Flaky Tests

Which of the following best describes a flaky test in software testing?

A A test that always fails due to a bug in the code.

B A test that is skipped during test execution.

C A test that runs slowly but always produces the same result.

D A test that sometimes passes and sometimes fails without any code changes.

💡 Hint

Think about tests that behave unpredictably without changes in the code.

A flaky test is one that produces different results (pass or fail) when run multiple times without any changes in the code or environment. This unpredictability makes it hard to trust test results.

❓ Predict Output

intermediate

Output of a Flaky Test Simulation

Consider the following Python test simulation code that randomly fails. What is the output after running it once?

Testing Fundamentals

import random

def flaky_test():
    if random.choice([True, False]):
        return 'Test Passed'
    else:
        return 'Test Failed'

result = flaky_test()
print(result)

A No output

B Test Failed

C SyntaxError

D Test Passed

💡 Hint

The output depends on random choice; consider one possible output.

The code randomly returns either 'Test Passed' or 'Test Failed'. Since the question asks for output after one run, both B and D are possible outputs.

❓ assertion

advanced

Correct Assertion to Detect Flaky Behavior

Which assertion approach best helps detect flaky tests by running the same test multiple times and checking for consistent results?

A Run the test multiple times and assert all results are the same.

B Run the test multiple times and assert at least one pass.

C Run the test once and assert the expected output.

D Run the test multiple times and assert at least one fail.

💡 Hint

Think about how to confirm a test is stable across runs.

To detect flaky tests, running the test multiple times and asserting that all results are consistent (all pass or all fail) helps identify instability. Option A ensures consistent results, which means the test is not flaky.

🔧 Debug

advanced

Debugging a Flaky Test Caused by Timing

Given this test code snippet, which change will most likely fix the flaky behavior caused by timing issues?

def test_load_data():
    data = load_data()
    assert len(data) > 0

# load_data fetches data asynchronously and may not be ready immediately

A Remove the assertion to avoid failures.

B Use a retry loop to check data readiness before asserting.

C Run the test only once to avoid flakiness.

D Add a fixed sleep delay before asserting the data length.

💡 Hint

Think about waiting for the data to be ready instead of guessing a fixed delay.

Using a retry loop to wait until the data is ready before asserting avoids flaky failures caused by timing. Fixed delays (Option B) can be unreliable and cause longer test times. Removing assertions or running once does not solve flakiness.

❓ framework

expert

Best Practice for Managing Flaky Tests in CI Pipelines

In a continuous integration (CI) pipeline, what is the best practice to handle flaky tests to maintain reliable test reports?

A Ignore flaky tests completely to avoid false failures.

B Remove flaky tests from the test suite permanently.

C Automatically retry flaky tests a fixed number of times before marking as failed.

D Run flaky tests only on developer machines, not in CI.

💡 Hint

Consider how to balance test reliability and visibility in automated pipelines.

Automatically retrying flaky tests a few times helps reduce false failures in CI reports while still keeping tests visible. Ignoring or removing flaky tests hides problems and reduces test coverage. Running only on developer machines reduces CI reliability.