Flaky test detection and retry in PyTest - Framework Patterns

Framework Mode - Flaky test detection and retry

Folder Structure

tests/
├── test_login.py
├── test_checkout.py
├── test_profile.py
conftest.py
pytest.ini
utils/
├── retry_helper.py
├── flaky_detector.py
reports/
└── latest_report.html

Test Framework Layers

• Tests: Individual test files inside tests/ folder, e.g., test_login.py. These contain test functions using pytest.
• Fixtures & Hooks: Defined in conftest.py for setup, teardown, and retry hooks.
• Utilities: Helper modules like retry_helper.py to implement retry logic and flaky_detector.py to log flaky test info.
• Configuration: pytest.ini to configure pytest plugins and retry options.
• Reports: Generated test reports stored in reports/ folder for review.

Configuration Patterns

• pytest.ini: Configure retry plugin with max retries and delay, e.g.,

  [pytest]
  addopts = --reruns 2 --reruns-delay 1
  

  This retries failed tests up to 2 times with 1 second delay.
• Environment Variables: Use environment variables or .env files to toggle flaky detection on/off without code changes.
• Custom Hooks: Use pytest hooks in conftest.py to detect flaky tests by tracking retries and logging flaky occurrences.

Test Reporting and CI/CD Integration

• Use pytest-html or Allure plugins to generate detailed HTML reports showing retry attempts and flaky test status.
• Reports saved in reports/ folder for easy access and historical comparison.
• Integrate test runs with CI/CD pipelines (e.g., GitHub Actions, Jenkins) to automatically run tests with retry enabled and fail builds on persistent failures.
• Use report annotations or comments in CI to highlight flaky tests for team review.

Best Practices

1. Limit retries: Retry only a small number of times (e.g., 2-3) to avoid masking real issues.
2. Log flaky tests: Keep track of tests that pass only after retries to identify unstable tests.
3. Use explicit waits: Combine retries with proper waits to reduce flakiness caused by timing issues.
4. Isolate flaky tests: Mark flaky tests with custom markers to run them separately if needed.
5. Review flaky tests regularly: Fix flaky tests promptly to maintain test suite reliability.

Self Check

Where in this folder structure would you add a new utility function to log flaky test occurrences during retries?

Key Result

Use pytest retry plugin with custom hooks and utilities to detect and handle flaky tests effectively.

Practice

1. What is the main purpose of marking a test as flaky in pytest using @pytest.mark.flaky(reruns=N)?

easy

A. To skip the test permanently

B. To automatically retry the test N times if it fails

C. To mark the test as slow and run it last

D. To run the test only once without retries

Solution

1. Step 1: Understand the flaky test concept

   Flaky tests fail randomly due to timing or environment issues, so retries help reduce false failures.

2. Step 2: Analyze the effect of @pytest.mark.flaky(reruns=N)

   This decorator tells pytest to rerun the test up to N times if it fails, increasing chances of passing despite flakiness.

3. Final Answer:

   To automatically retry the test N times if it fails -> Option B

4. Quick Check:

   Flaky test retry = reruns N times [OK]

Hint: Retries mean automatic rerun on failure [OK]

Common Mistakes:

• Confusing flaky with skipped tests
• Thinking flaky marks slow tests
• Assuming flaky disables retries

2. Which of the following is the correct syntax to retry a flaky test 3 times in pytest?

easy

A. @pytest.flaky(tries=3)

B. @pytest.retry(3)

C. @pytest.mark.retry(3)

D. @pytest.mark.flaky(reruns=3)

Solution

1. Step 1: Recall the correct decorator name and parameter

   The correct decorator is @pytest.mark.flaky with parameter reruns to specify retry count.

2. Step 2: Match syntax with options

   @pytest.mark.flaky(reruns=3) uses @pytest.mark.flaky(reruns=3), which is the correct syntax for retrying 3 times.

3. Final Answer:

   @pytest.mark.flaky(reruns=3) -> Option D

4. Quick Check:

   Decorator name + reruns param = correct syntax [OK]

Hint: Use @pytest.mark.flaky with reruns param [OK]

Common Mistakes:

• Using @pytest.retry instead of @pytest.mark.flaky
• Using wrong parameter names like tries
• Missing the mark prefix

3. Given this pytest test code snippet, what will be the output if the test randomly fails once and passes on retry?

import pytest

@pytest.mark.flaky(reruns=2)
def test_random():
    import random
    assert random.choice([True, False])

medium

A. Test may pass after 1 or 2 retries if random returns True

B. Test always fails because random.choice is unpredictable

C. Test runs only once and fails if random returns False

D. Test is skipped due to flaky mark

Solution

1. Step 1: Understand the flaky decorator effect

   The test will rerun up to 2 times if it fails, allowing multiple chances to pass.

2. Step 2: Analyze the random assertion

   The assertion randomly passes or fails. If it fails once, retry can pass if random returns True on retry.

3. Final Answer:

   Test may pass after 1 or 2 retries if random returns True -> Option A

4. Quick Check:

   Retries allow passing despite random failure [OK]

Hint: Retries give multiple chances to pass random failures [OK]

Common Mistakes:

• Assuming flaky skips tests
• Thinking test always fails on first try
• Ignoring retry count effect

4. You wrote this flaky test with retry but it never retries on failure. What is the error?

import pytest

@pytest.mark.flaky(rerun=3)
def test_example():
    assert False

medium

A. The test must return True to retry

B. The decorator should be @pytest.retry, not @pytest.mark.flaky

C. The parameter name should be 'reruns', not 'rerun'

D. The test cannot retry if it always fails

Solution

1. Step 1: Check the decorator parameter spelling

   The correct parameter for retry count is reruns, not rerun.

2. Step 2: Understand impact of wrong parameter

   Using rerun is ignored by pytest, so no retries happen despite failures.

3. Final Answer:

   The parameter name should be 'reruns', not 'rerun' -> Option C

4. Quick Check:

   Correct param spelling = reruns [OK]

Hint: Check exact parameter spelling: reruns, not rerun [OK]

Common Mistakes:

• Misspelling reruns parameter
• Using wrong decorator name
• Expecting retries on always failing test

5. You want to reduce false failures from a flaky test that sometimes fails due to timing issues. Which approach best combines flaky test detection and retry in pytest?

hard

A. Use @pytest.mark.flaky(reruns=3) and add explicit wait in test code

B. Use @pytest.mark.skip to ignore the flaky test

C. Remove retries and fix test to always pass

D. Use @pytest.mark.flaky(reruns=0) to disable retries

Solution

1. Step 1: Understand flaky test retry purpose

   Retries help reduce false failures by rerunning tests that fail randomly.

2. Step 2: Combine retry with test stabilization

   Adding explicit waits addresses timing issues, improving test stability alongside retries.

3. Step 3: Evaluate other options

   Skipping ignores tests, removing retries loses retry benefit, and reruns=0 disables retries.

4. Final Answer:

   Use @pytest.mark.flaky(reruns=3) and add explicit wait in test code -> Option A

5. Quick Check:

   Retries + waits = best flaky test handling [OK]

Hint: Combine retries with waits to fix flaky timing issues [OK]

Common Mistakes:

• Skipping flaky tests instead of fixing
• Disabling retries by setting reruns=0
• Ignoring timing issues causing flakiness