Parallel test execution in Testing Fundamentals - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Why use parallel test execution?

Which of the following is the main benefit of running tests in parallel?

A It guarantees that tests will never fail due to shared resources.

B It ensures tests run in a specific order to avoid conflicts.

C It reduces the total time needed to run all tests by running multiple tests at the same time.

D It automatically fixes bugs found during testing.

💡 Hint

Think about how doing many things at once affects total time.

Parallel test execution runs multiple tests simultaneously, which reduces the total testing time. It does not guarantee order or fix bugs automatically.

❓ Predict Output

intermediate

Output of parallel test simulation

Consider this Python code simulating parallel test execution with threads. What is the output?

Testing Fundamentals

import threading
import time
results = []
def test(id):
    time.sleep(0.1)
    results.append(f"Test{id} done")
threads = [threading.Thread(target=test, args=(i,)) for i in range(3)]
for t in threads:
    t.start()
for t in threads:
    t.join()
print(results)

A []

B ['Test2 done', 'Test1 done', 'Test0 done']

C ['Test0 done', 'Test1 done', 'Test2 done']

D ['Test0 done']

💡 Hint

All threads complete before printing results, but order is not guaranteed.

Threads append results after sleeping. Because threads run concurrently, the order of results in the list is not guaranteed and can vary between runs.

❓ assertion

advanced

Correct assertion for parallel test results

You run tests in parallel and collect results in a list. Which assertion correctly checks that all expected tests completed?

Testing Fundamentals

expected = {'Test1', 'Test2', 'Test3'}
results = ['Test3', 'Test1', 'Test2']

A assert set(results) == expected

B assert results == expected

C assert all(test in results for test in expected)

D assert results.sort() == expected.sort()

💡 Hint

Think about comparing collections ignoring order.

Using set comparison checks that all expected tests are present regardless of order. Option A fails because list and set are different types. Option A is true but less strict (does not check for extra items). Option A is invalid because list.sort() returns None.

🔧 Debug

advanced

Identify the cause of flaky tests in parallel execution

Tests run fine individually but sometimes fail when run in parallel. What is the most likely cause?

A Tests have syntax errors causing random failures.

B Tests use different programming languages.

C Tests run too slowly and timeout.

D Tests share and modify the same global data without isolation.

💡 Hint

Think about what changes when tests run at the same time.

Sharing global data without isolation causes race conditions and flaky failures in parallel runs. Syntax errors or slow tests would fail consistently. Different languages do not cause flaky behavior.

❓ framework

expert

Configuring parallel test execution in a test framework

In a popular test framework, which configuration option enables running tests in parallel across multiple CPU cores?

A pytest -n auto

B pytest --disable-parallel

C pytest --max-workers=1

D pytest --sequential

💡 Hint

Look for the option that automatically uses multiple cores.

The option -n auto in pytest-xdist plugin runs tests in parallel using all available CPU cores. Other options disable or limit parallelism.