sik-stochastic-tests 0.0.6

A pytest plugin for testing stochastic systems like LLMs, providing statistical confidence through multiple test runs.

sik-stochastic-tests

A pytest plugin for testing non-deterministic systems such as LLMs, running tests multiple times with configurable sample sizes and success thresholds to establish reliable results despite occasional random failures.

Overview

When testing non-deterministic systems such as large language models, traditional pass/fail testing is problematic because of sporadic errors or response inconsistencies. This plugin allows you to run tests multiple times and determine success based on a threshold, ensuring your tests are reliable even with occasional random failures.

Features

• Run tests multiple times with a single decorator
• Set success thresholds to allow for occasional failures
• Batch test execution for performance optimization
• Retry capability for flaky tests
• Timeout control for long-running tests
• Detailed reporting of success rates and failure patterns
• Full support for both synchronous and asynchronous tests

Installation

pip install sik-stochastic-tests

Or with uv:

uv add sik-stochastic-tests

Usage

Basic Usage

Mark any test with the stochastic decorator to run it multiple times:

import pytest

@pytest.mark.stochastic(samples=5)  # Run 5 times
def test_llm_response():
    response = my_llm.generate("What is the capital of France?")
    assert 'paris' in response.lower()

Async Tests

For asynchronous tests, you must use both the @pytest.mark.asyncio and @pytest.mark.stochastic decorators:

import pytest

@pytest.mark.asyncio  # Required for async tests
@pytest.mark.stochastic(samples=5)
async def test_async_llm_response():
    response = await my_async_llm.generate("What is the capital of France?")
    assert 'paris' in response.lower()

Important: The @pytest.mark.asyncio decorator is required for async tests. Make sure you have pytest-asyncio installed.

Setting a Success Threshold

You can specify what percentage of runs must succeed for the test to pass:

@pytest.mark.stochastic(samples=10, threshold=0.8)  # 80% must pass
def test_with_threshold():
    # This test will pass if at least 8 out of 10 runs succeed
    result = random_function()
    assert result > 0

Retrying Flaky Tests

Specify which exceptions should trigger retries:

@pytest.mark.stochastic(
    samples=5,
    retry_on=[ConnectionError, TimeoutError],
    max_retries=3
)
def test_with_retry():
    response = api_call()  # Might occasionally fail with connection issues
    assert response.status_code == 200

Handling Timeouts

Set a timeout for long-running tests:

# For synchronous tests
@pytest.mark.stochastic(samples=3, timeout=5.0)  # 5 second timeout
def test_with_timeout():
    result = long_running_operation()
    assert result.is_valid

# For asynchronous tests
@pytest.mark.asyncio
@pytest.mark.stochastic(samples=3, timeout=5.0)  # 5 second timeout
async def test_async_with_timeout():
    result = await async_long_running_operation()
    assert result.is_valid

Batch Processing

Control concurrency with batch processing:

# Works for both sync and async tests
@pytest.mark.stochastic(samples=20, batch_size=5)  # Run 5 at a time
def test_with_batching():
    result = my_operation()
    assert result.success

@pytest.mark.asyncio
@pytest.mark.stochastic(samples=20, batch_size=5)  # Run 5 at a time
async def test_async_with_batching():
    result = await async_operation()
    assert result.success

Disabling Stochastic Mode

Temporarily disable stochastic behavior with a command-line flag:

pytest --disable-stochastic

This will run each test only once, ignoring the stochastic parameters for both sync and async tests.

Advanced Examples

Testing LLM Outputs

@pytest.mark.stochastic(samples=10, threshold=0.7)
def test_llm_instruction_following():
    prompt = "Write a haiku about programming"
    response = llm.generate(prompt)
    
    # Test passes if at least 70% of responses contain all these criteria
    assert len(response.split("\n")) == 3, "Should have 3 lines"
    assert "program" in response.lower(), "Should mention programming"
    
    # Count syllables (simplified)
    lines = response.split("\n")
    syllable_counts = [count_syllables(line) for line in lines]
    assert syllable_counts == [5, 7, 5], f"Should follow 5-7-5 pattern, got {syllable_counts}"

Testing with External APIs (Async)

@pytest.mark.asyncio  # Required for async tests
@pytest.mark.stochastic(
    samples=5, 
    threshold=0.8,
    retry_on=[requests.exceptions.RequestException],
    max_retries=3,
    timeout=10.0
)
async def test_weather_api():
    response = await fetch_weather("New York")
    
    # Basic schema validation
    assert "temperature" in response
    assert "humidity" in response
    assert "wind_speed" in response
    
    # Reasonable values check
    assert -50 <= response["temperature"] <= 50  # Celsius
    assert 0 <= response["humidity"] <= 100      # Percentage

Combining Multiple Features

# Synchronous example with multiple features
@pytest.mark.stochastic(
    samples=20,
    threshold=0.9,
    batch_size=5,
    retry_on=[ConnectionError, TimeoutError],
    max_retries=2,
    timeout=3.0
)
def test_complex_scenario():
    # This test will:
    # - Run 20 times total
    # - Run 5 at a time (batched)
    # - Pass if at least 18 runs succeed (90% threshold)
    # - Retry on connection or timeout errors (up to 2 retries)
    # - Timeout after 3 seconds for each run
    result = complex_operation()
    assert result.is_successful

# Asynchronous equivalent
@pytest.mark.asyncio
@pytest.mark.stochastic(
    samples=20,
    threshold=0.9,
    batch_size=5,
    retry_on=[ConnectionError, TimeoutError],
    max_retries=2,
    timeout=3.0
)
async def test_async_complex_scenario():
    result = await async_complex_operation()
    assert result.is_successful

Test Result Output

The plugin provides detailed test results in the console for both synchronous and asynchronous tests:

=========================== Stochastic Test Results ===========================

test_llm.py::test_llm_instruction_following:
  Success rate: 0.80
  Runs: 10, Successes: 8, Failures: 2
  Failure samples:
    1. AssertionError: Should follow 5-7-5 pattern, got [4, 6, 5]
    2. AssertionError: Should mention programming

test_async.py::test_async_weather_api:
  Success rate: 0.60
  Runs: 5, Successes: 3, Failures: 2
  Failure samples:
    1. TimeoutError: Test timed out after 10.0 seconds
    2. AssertionError: 'temperature' not in response

Requirements for Async Tests

1. pytest-asyncio must be installed:

   pip install pytest-asyncio
   

2. Each async test must have both decorators:

   @pytest.mark.asyncio
   @pytest.mark.stochastic(...)
   async def test_something():
       ...
   

3. For pytest.ini configuration, you might want to set:

   [pytest]
   asyncio_mode = auto
   

Configuration

You can configure default behavior in your pyproject.toml or pytest.ini file:

[tool.pytest.ini_options]
# Set option to exclude stochastic tests from certain environments
addopts = "--disable-stochastic"

Compatibility

• Python 3.11+
• pytest 8.0+
• pytest-asyncio 0.21+ (for async tests)

Troubleshooting

Async Tests Not Running Multiple Times

If your async tests are only running once instead of the specified number of samples:

1. Check that you have both @pytest.mark.asyncio and @pytest.mark.stochastic decorators
2. Ensure pytest-asyncio is installed
3. Check that --disable-stochastic flag is not present

Timeout Not Working for Async Tests

Timeouts for async tests are implemented using asyncio.wait_for(). If your timeouts aren't working:

1. Ensure you're using a recent version of this plugin
2. Make sure your test is actually running asynchronously

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

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