Flaky Test Detector

Identify and fix non-deterministic tests that intermittently fail without code changes.

Quick Start

When a user reports flaky tests or asks for test reliability analysis:

1. Identify the approach: Determine if analyzing code patterns or test execution results
2. Analyze for flakiness: Look for common flaky patterns in test code or execution history
3. Report findings: List identified flaky tests with specific issues
4. Suggest fixes: Provide concrete remediation strategies

What Makes Tests Flaky

Flaky tests fail intermittently without code changes due to:

• Timing issues: Race conditions, fixed sleeps, async/await problems
• State management: Shared state between tests, improper cleanup
• External dependencies: Network calls, database connections, file system
• Randomness: Unseeded random data, UUID generation
• Time dependencies: Current time/date, timezone assumptions
• Resource issues: Leaks, insufficient cleanup
• Test order: Dependencies between tests
• Environment: Hardcoded paths, missing env vars

Detection Methods

Static Code Analysis

Analyze test code for common flaky patterns.

When to use:

• Reviewing test code for potential issues
• Proactive flakiness prevention
• Code review of new tests
• Refactoring existing tests

Process:

1. Read test files
2. Search for flaky patterns (see flaky-patterns.md)
3. Identify specific issues with line numbers
4. Suggest fixes (see remediation-strategies.md)

Common patterns to detect:

Timing issues:

• time.sleep()

  ,

  Thread.sleep()

  - Fixed waits
• Missing

  await

  in async functions
• Race conditions with threading

State issues:

• Class or global variables in test classes
• Missing setUp/tearDown or fixtures
• Database operations without cleanup

External dependencies:

• requests.get()

  ,

  http.client

  - Real network calls
• Database connections to production/external DBs
• File operations without temp directories

Randomness:

• random.

  without seed

• UUID.randomUUID()

  without mocking
• Non-deterministic data generation

Time dependencies:

• datetime.now()

  ,

  System.currentTimeMillis()

• Timezone-dependent assertions
• Date comparisons without mocking

Test Result Analysis

Analyze test execution history to find inconsistent results.

When to use:

• Tests are failing intermittently in CI/CD
• Investigating specific test reliability
• Analyzing test suite health
• Tracking flakiness over time

Process:

1. Collect test results from multiple runs
2. Use

   scripts/analyze_test_results.py

   to analyze patterns
3. Review flakiness scores and patterns
4. Investigate high-scoring tests

Script usage:

python scripts/analyze_test_results.py test_results.json

Input format (JSON):

[
  {
    "test_name": "test_user_login",
    "status": "passed",
    "timestamp": "2024-01-01T10:00:00",
    "duration": 1.23
  },
  {
    "test_name": "test_user_login",
    "status": "failed",
    "timestamp": "2024-01-01T11:00:00",
    "duration": 1.45
  }
]

Metrics:

• Flakiness score: 0-1, higher = more flaky (based on pass rate variance)
• Pass rate: Percentage of successful runs
• Pattern: Recent pass/fail sequence (P = pass, F = fail)
• Alternating: Whether test alternates between pass/fail
• Duration variance: Inconsistent execution time indicates issues

Framework-Specific Guidance

Python (pytest, unittest)

Common issues:

• Missing fixtures or improper fixture scope
• Shared class variables
• Not using

  tmp_path

  for file operations
• Missing

  @pytest.mark.django_db

  for database tests
• Unseeded

  random

  module usage

Best practices:

• Use fixtures for test data and cleanup
• Use

  tmp_path

  fixture for file operations
• Mock external calls with

  pytest-mock

  or

  unittest.mock

• Use

  freezegun

  for time mocking
• Seed random with

  random.seed()

Java (JUnit, TestNG)

Common issues:

• Static variables in test classes
• Missing

  @Before

  /

  @After

  cleanup
• Not using

  @Transactional

  for database tests
• Fixed

  Thread.sleep()

  calls
• Hardcoded file paths

Best practices:

• Use

  @Before

  /

  @After

  for setup/cleanup
• Use

  @Transactional

  for automatic rollback
• Mock with Mockito
• Use

  Clock

  for time mocking
• Use try-with-resources for resource management

Workflow

1. Understand the Context

Ask clarifying questions:

• What tests are flaky?
• How often do they fail?
• What's the failure pattern?
• Any recent changes?
• CI/CD or local environment?

2. Choose Detection Method

Static analysis if:

• Reviewing code proactively
• No test execution history available
• Want to prevent flakiness

Result analysis if:

• Have test execution history
• Tests failing intermittently
• Need to quantify flakiness

3. Analyze for Flakiness

For static analysis:

• Read test files
• Search for patterns from flaky-patterns.md
• Note specific issues with line numbers
• Categorize by issue type

For result analysis:

• Run

  analyze_test_results.py

  script
• Review flakiness scores
• Identify high-risk tests
• Examine failure patterns

4. Report Findings

Structure the report:

• Summary: Number of flaky tests found
• High priority: Tests with highest flakiness scores
• By category: Group by issue type
• Specific issues: File paths and line numbers

Example format:

Found 5 potentially flaky tests:

HIGH PRIORITY:
- test_user_login (flakiness: 0.85)
  - Line 45: time.sleep(2) - fixed wait
  - Line 52: Shared class variable 'user_data'

MEDIUM PRIORITY:
- test_api_call (flakiness: 0.62)
  - Line 23: requests.get() - unmocked network call

5. Suggest Remediation

For each issue, provide:

• What's wrong: Explain the flaky pattern
• Why it's flaky: Describe the non-determinism
• How to fix: Concrete code example

Reference remediation-strategies.md for detailed fixes.

Example Usage Patterns

User: "Our test_checkout test keeps failing randomly" → Analyze test code for flaky patterns, report findings with fixes

User: "Find all flaky tests in the test suite" → Scan all test files for common flaky patterns

User: "This test has a 60% pass rate, why?" → Analyze test code and suggest specific fixes

User: "Analyze these test results for flakiness" → Use analyze_test_results.py script on provided data

User: "How do I fix this race condition in my test?" → Provide remediation strategy with code examples

User: "Review this test for potential flakiness" → Static analysis of specific test with recommendations

Best Practices

Detection

• Look for multiple flaky patterns, not just one
• Consider the test framework's idioms
• Check both test code and test fixtures/setup
• Review recent changes that might introduce flakiness

Reporting

• Prioritize by severity and frequency
• Provide specific line numbers
• Group related issues together
• Include confidence level in assessment

Remediation

• Suggest framework-appropriate fixes
• Provide complete code examples
• Explain why the fix works
• Consider test maintainability

Prevention

• Recommend test design patterns
• Suggest CI/CD improvements (retry policies, test isolation)
• Encourage test independence
• Promote proper mocking and fixtures