Run Tests

Test Suite Validation Skill

This skill helps you efficiently validate code changes by running the appropriate subset of the test suite. It uses scripts/run-tests to intelligently discover affected tests and run only what's necessary for validation.

When to Use This Skill

Use this skill when you have:

• Made changes to source code files and want to validate they work
• Fixed a bug and want to verify the fix
• Added a feature and need test coverage
• Modified test infrastructure or configuration
• Want to verify changes don't break existing functionality

Key Principles

1. Always use the run-tests skill when testing code changes - it's optimized for intelligent suite discovery
2. Never run pytest directly - bypasses the project's test infrastructure (use scripts/run-tests or riot via scripts/ddtest)
3. Minimal venvs for iteration - run 1-2 venvs initially, expand only if needed
4. Use --dry-run first - see what would run before executing
5. Follow official docs - docs/contributing-testing.rst is the source of truth for testing procedures

How This Skill Works

Step 1: Identify Changed Files

First, determine which files were modified:

• If you have pending edits, I'll identify the changed files from the current session
• I'll look at git status to find staged, unstaged, and untracked changes
• You can also specify files explicitly if working on specific changes

Step 2: Discover Available Test Suites

I'll use the scripts/run-tests script to discover what test suites match your changes:

scripts/run-tests --list <edited-files>

This outputs JSON showing:

• Available test suites that match your changed files
• All venvs (Python versions + package combinations) available for each suite
• Their hashes, Python versions, and package versions

Step 3: Intelligently Select Venvs

Rather than running ALL available venvs (which could take hours), I'll select the minimal set needed to validate your changes:

For Core/Tracing Changes (Broad Impact)

When you modify files like:

• ddtrace/internal/core/*, ddtrace/_trace/*, ddtrace/trace/*
• ddtrace/_monkey.py, ddtrace/settings/*
• ddtrace/constants.py

Strategy: Run core tracer + internal tests with 1 venv each

• Example: tracer suite with latest Python + internal suite with latest Python
• This validates broad-reaching changes without excessive overhead
• Skip integration suites unless the change directly affects integration code

For Integration/Contrib Changes (Targeted Impact)

When you modify files like:

• ddtrace/contrib/flask/*, ddtrace/contrib/django/*, etc.
• ddtrace/contrib/*/patch.py or integration-specific code

Strategy: Run ONLY the affected integration suite with 1-2 venvs

• Example: Flask changes → run contrib::flask suite with latest Python
• If change involves multiple versions (e.g., Django 3.x and 4.x), pick 1 venv per major version
• Skip unrelated integrations

For Test-Only Changes

When you modify tests/ files (but not test infrastructure):

• Run only the specific test files/functions modified
• Use pytest args: -- -- -k test_name or direct test file paths

For Test Infrastructure Changes

When you modify:

• tests/conftest.py, tests/suitespec.yml, scripts/run-tests, riotfile.py

Strategy: Run a quick smoke test suite

• Example: internal suite with 1 venv as a sanity check
• Or run small existing test suites to verify harness changes

Step 4: Execute Selected Venvs

I'll run the selected venvs using:

scripts/run-tests --venv <hash1> --venv <hash2> ...

This will:

• Start required Docker services (redis, postgres, etc.)
• Run tests in the specified venvs sequentially
• Stop services after completion
• Show real-time output and status

Step 5: Handle Results

If tests pass: ✅ Your changes are validated!

If tests fail: 🔴 I'll:

• Show you the failure details
• Identify which venv failed
• Ask clarifying questions to understand the issue
• Offer to run specific failing tests with more verbosity
• Help iterate on fixes and re-run

For re-running specific tests:

scripts/run-tests --venv <hash> -- -- -vv -k test_name

When Tests Fail

When you encounter test failures, follow this systematic approach:

1. Review the failure details carefully - Don't just skim the error, understand what's actually failing
2. Understand what's failing - Don't blindly re-run; analyze the root cause
3. Make code changes - Fix the underlying issue
4. Re-run with more verbosity if needed - Use -vv or -vvv for detailed output
5. Iterate until tests pass - Repeat the process with each fix

Venv Selection Strategy in Detail

Understanding Venv Hashes

From scripts/run-tests --list, you'll see output like:

{
  "suites": [
    {
      "name": "tracer",
      "venvs": [
        {
          "hash": "abc123",
          "python_version": "3.8",
          "packages": "..."
        },
        {
          "hash": "def456",
          "python_version": "3.11",
          "packages": "..."
        }
      ]
    }
  ]
}

Selection Rules

1. Latest Python version is your default choice

   • Unless your change specifically targets an older Python version
   • Example: if fixing Python 3.8 compatibility, also test 3.8

2. One venv per suite is usually enough for iteration

   • Only run multiple venvs per suite if:
     • Change impacts multiple Python versions differently
     • Testing package compatibility variations (e.g., Django 3.x vs 4.x)
     • Initial validation passed and you want broader coverage

3. Minimize total venvs

   • 1-2 venvs total for small targeted changes
   • 3-4 venvs maximum for broader changes
   • Never run 10+ venvs for initial validation (save that for CI)

4. Consider test runtime

   • Each venv can take 5-30 minutes depending on suite
   • With 2 venvs you're looking at 10-60 minutes for iteration
   • With 5 venvs you're looking at 25-150 minutes
   • Scale appropriately for your patience and deadline

Using --venv Directly

When you have a specific venv hash you want to run, you can use it directly without specifying file paths:

scripts/run-tests --venv e06abee

The --venv flag automatically searches all available venvs across all suites, so it works regardless of what files you have locally changed. This is useful when:

• You know exactly which venv you want to test
• You have unrelated local changes that would otherwise limit suite matching
• You want to quickly re-run a specific venv without file path arguments

Examples

Example 1: Fixing a Flask Integration Bug

Changed file: ddtrace/contrib/internal/flask/patch.py

scripts/run-tests --list ddtrace/contrib/internal/flask/patch.py
# Output shows: contrib::flask suite available

# Select output (latest Python):
# Suite: contrib::flask
# Venv: hash=e06abee, Python 3.13, flask

# Run with --venv directly (searches all venvs automatically)
scripts/run-tests --venv e06abee
# Runs just Flask integration tests

Example 2: Fixing a Core Tracing Issue

Changed file: ddtrace/_trace/tracer.py

scripts/run-tests --list ddtrace/_trace/tracer.py
# Output shows: tracer suite, internal suite available

# Select strategy:
# - tracer: latest Python (e.g., abc123)
# - internal: latest Python (e.g., def456)

# Run with --venv directly (searches all venvs automatically)
scripts/run-tests --venv abc123 --venv def456
# Validates core tracer and internal components

Example 3: Fixing a Test-Specific Bug

Changed file: tests/contrib/flask/test_views.py

scripts/run-tests --list tests/contrib/flask/test_views.py
# Output shows: contrib::flask suite

# Run just the specific test:
scripts/run-tests --venv flask_py311 -- -- -vv tests/contrib/flask/test_views.py

Example 4: Iterating on a Failing Test

First run shows one test failing:

scripts/run-tests --venv flask_py311 -- -- -vv -k test_view_called_twice
# Focused on the specific failing test with verbose output

Best Practices

DO ✅

• Start small: Run 1 venv first, expand only if needed
• Be specific: Use pytest -k filter when re-running failures
• Check git: Verify you're testing the right files with git status
• Read errors: Take time to understand test failures before re-running
• Ask for help: When unclear what tests to run, ask me to analyze the changes

DON'T ❌

• Run all venvs initially: That's what CI is for
• Skip the minimal set guidance: It's designed to save you time
• Ignore service requirements: Some suites need Docker services up
• Run tests without changes saved: Make sure edits are saved first
• Iterate blindly: Understand what's failing before re-running

Additional Testing Resources

For comprehensive testing guidance, refer to the contributing documentation:

• docs/contributing-testing.rst - Detailed testing guidelines

  • What kind of tests to write (unit tests, integration tests, e2e tests)
  • When to write tests (feature development, bug fixes)
  • Where to put tests in the repository
  • Prerequisites (Docker, uv)
  • Complete scripts/run-tests usage examples
  • Riot environment management details
  • Running specific test files and functions
  • Test debugging strategies

• docs/contributing.rst - PR and testing requirements

  • All changes need tests or documented testing strategy
  • How tests fit into the PR review process
  • Testing expectations for different types of changes

• docs/contributing-design.rst - Test architecture context

  • How products, integrations, and core interact
  • Where different types of tests should live
  • Testing patterns for each library component

When to reference these docs:

• First time writing tests for this project → Read contributing-testing.rst
• Understanding test requirements for PRs → Read contributing.rst
• Need context on test architecture → Read contributing-design.rst

Troubleshooting

Docker services won't start

# Manually check/stop services:
docker compose ps
docker compose down

Can't find matching suites

• Verify the file path is correct
• Check tests/suitespec.yml to understand suite patterns
• Your file might not be covered by any suite pattern yet

Test takes too long

• You may have selected too many venvs
• Try running with just 1 venv
• Use pytest -k to run subset of tests

Technical Details

Architecture

The scripts/run-tests system:

• Maps source files to test suites using patterns in tests/suitespec.yml
• Uses riot to manage multiple Python/package combinations as venvs
• Each venv is a self-contained environment
• Docker services are managed per suite lifecycle
• Tests can pass optional pytest arguments with -- --

Supported Suite Types

Primary suites for validation:

• tracer: Core tracing functionality tests
• internal: Internal component tests
• contrib::*: Integration with specific libraries (flask, django, etc.)
• integration_*: Cross-library integration scenarios
• Specialized: telemetry, profiling, appsec, llmobs, etc.

Environment Variables

Some suites require environment setup:

• DD_TRACE_AGENT_URL: For snapshot-based tests
• Service-specific variables for Docker containers
• These are handled automatically by the script

Resource Limiting (for testing under constrained environments)

You can limit CPU and memory resources to simulate resource-constrained CI environments where multiple jobs run in parallel. This helps reproduce flaky tests that fail due to timing issues, race conditions, or resource exhaustion.

Environment Variables:

• DD_TEST_CPUS: CPU limit (e.g., 0.25, 0.5, 1.0, 2.0)
• DD_TEST_MEMORY: Memory limit with unit (e.g., 512m, 1g, 2g)

Usage:

# Run tests with resource constraints
DD_TEST_CPUS=0.5 DD_TEST_MEMORY=1g scripts/run-tests --venv <hash>

# Run specific test file with heavy constraints
DD_TEST_CPUS=0.25 DD_TEST_MEMORY=1g scripts/run-tests tests/path/to/test.py

# Multiple runs to catch intermittent failures
for i in {1..10}; do
  DD_TEST_CPUS=0.5 DD_TEST_MEMORY=1g scripts/run-tests --venv <hash> -- -- --randomly-seed=$RANDOM
done

Recommended Resource Limits:

• Moderate Load (Typical Shared CI):

  DD_TEST_CPUS=2.0 DD_TEST_MEMORY=4g
  

  Simulates a CI runner with some other jobs running. Good for initial testing.

• Heavy Load (Busy CI Server):

  DD_TEST_CPUS=1.0 DD_TEST_MEMORY=2g
  

  Simulates a heavily loaded CI server with many concurrent jobs. Recommended starting point for reproducing flaky tests.

• Extreme Load (Stress Testing):

  DD_TEST_CPUS=0.5 DD_TEST_MEMORY=1g
  

  Simulates extreme resource contention. Good for surfacing timing issues and race conditions.

• Critical Failure Conditions:

  DD_TEST_CPUS=0.25 DD_TEST_MEMORY=512m
  

  Forces maximum resource pressure. Use this to find the breaking point or reproduce worst-case scenarios.

When to Use Resource Limits:

1. Investigating flaky tests - Tests that pass locally but fail in CI
2. Timing-sensitive tests - Tests involving async operations, network calls, or multiprocessing
3. Resource exhaustion issues - Tests that fail under memory pressure or CPU throttling
4. Race condition detection - Slower execution can expose timing bugs
5. Reproducing CI failures - When you have a seed that failed in CI

Verifying Limits Are Applied:

# Check configuration before running
DD_TEST_CPUS=0.5 DD_TEST_MEMORY=1g docker compose config | grep -A 5 testrunner

# Monitor actual resource usage during test run (in another terminal)
docker stats

Example: Testing a Flaky Test

# Run a known flaky test 20 times with resource constraints
DD_TEST_CPUS=0.5 DD_TEST_MEMORY=1g scripts/run-tests \
  tests/appsec/integrations/flask_tests/test_iast_flask_testagent.py::test_iast_unvalidated_redirect \
  -- -- --count=20