awesome-python-testing

Collection of awesome 😎️ Python resources for testing
https://github.com/cleder/awesome-python-testing

Last synced: about 9 hours ago
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Assertions
- believe - A python package for json/dictionary validation.
- easycheck - A collection of assertion-like functions to be used in code, where assertion themselves should be avoided; `easycheck` includes also function aliases to be used in unit testing.
- expects - Expects is an expressive and extensible TDD/BDD assertion library for Python.
- expycted - Another Python expect pattern implementation. Simple, intuitive and approachable, with ability to plug in to any testing framework that relies on assertions.
- dirty-equals - A python library that (mis)uses the `__eq__` method to make python code (generally unit tests) more declarative and therefore easier to read and write.
- Precisely - Write precise assertions so you only test the behaviour you're really interested in.
- PyHamcrest - A framework for writing matcher objects, allowing you to declaratively define "match" rules.
- pytest_cache_assert - Cache assertion data to simplify regression testing of complex serializable data.
- sure - An idiomatic assertion toolkit with human-friendly failure messages, inspired by RSpec Expectations and should.js.
Behavior-driven Development
- behave - is behavior-driven development, Python style.
- lettuce - Behavior-driven-development tool for python, inspired by Cucumber for Ruby.
- pytest-pyspec - Transforms pytest output into a beautiful, readable format similar to RSpec. It provides semantic meaning to your tests by organizing them into descriptive hierarchies.
- mamba - The definitive testing tool for Python. Born under the banner of BDD.
Code Coverage
- coverage-conditional-plugin - Conditional coverage based on any rules you define.
- diff_cover - Automatically find diff lines that need test coverage.
- Coverage.py - is a tool for measuring code coverage of Python programs.
Design by Contract
- deal - Design by contract for Python with static checker and test generation.
- icontract - Design-by-contract in Python3 with informative violation messages and inheritance.
- pact-python - Python version of Pact. Enables consumer driven contract testing, providing a mock service and DSL for the consumer project, and interaction playback and verification for the service provider project.
Fake Data
- autofaker - designed to minimize the setup/arrange phase of your unit tests by removing the need to manually write code to create anonymous variables as part of a test cases setup/arrange phase.
- faker - A Python package that generates fake data.
- fake2db - Fake database generator.
- genuine-fake - Genuine Fake means an imitation of a (usually) valuable object that is so good that it is, to all intents and purposes, identical.
- mimesis - A Python library that helps you generate fake data.
- radar - Generate random datetime / time.
Fixtures
- protestr - A simple, powerful fixture provider for Python tests. Protestr's intuitive API lets you generate versatile fixtures for your test cases and inject them as dependencies on demand.
- pytest-deadfixtures - A simple plugin to list unused or duplicated fixtures in a pytest suite.
- pytest-mysql - A pytest plugin, that enables you to test your code that relies on a running MySQL Database. It allows you to specify fixtures for MySQL process and client.
- pytest-rabbitmq - A pytest plugin, that enables you to test your code that relies on a running RabbitMQ server. It allows you to specify fixtures for RabbitMQ server and client.
LLM & MCP Testing
- mcp-server-fuzzer - A comprehensive fuzzing tool designed specifically for testing Model Context Protocol (MCP) servers. It supports both tool argument fuzzing and protocol type fuzzing across multiple transport protocols.
- Tenro - An open-source, provider-agnostic testing framework for AI agents that integrates with pytest. It simulates LLM and tool calls to test edge cases, failure paths, and agent logic without live API calls.
Load Testing
- Dynamic Workload Model - Code to generate dynamic workload model. Useful for testing autoscaling in cloud or mimicking different load profile for different scenario.
- Grasshopper - A lightweight framework for performing load tests against an environment, primarily against an API. Grasshopper glues Locust, Pytest, some plugins (namely Locust InfluxDBListener ) and some custom code to provide a package that makes authoring load tests simple with very little boilerplate needed.
- Grizzly - is a framework to be able to easily define load scenarios, and is mainly built on-top of Locust and Behave.
- Locust - Scalable user load testing tool written in Python.
- pynonymizer - is a universal tool for translating sensitive production database dumps into anonymized copies.
- pynonymizer - is a universal tool for translating sensitive production database dumps into anonymized copies.
Memory Management
- tracemalloc - is a debug tool to trace memory blocks allocated by Python.
- filprofiler - A Python memory profiler for data processing and scientific computing applications.
- Guppy 3 - a Python programming environment & heap analysis toolset.
- mem_top - shows top suspects for memory leaks in your Python program.
- Pympler - is a development tool to measure, monitor and analyze the memory behavior of Python objects in a running Python application.
Mock and Stub
- mock - (Python standard library) A mocking and patching library.
- Aioresponses - is a helper for mock/fake web requests in python aiohttp package.
- Cornell - record & replay mock server.
- Flexmock - is a testing library for Python that makes it easy to create mocks, stubs and fakes.
- freezegun - Travel through time by mocking the datetime module.
- httmock - A mocking library for requests for Python 2.6+ and 3.2+.
- httpretty - HTTP request mock tool for Python.
- Kesha - A web service with a user interface for testing http requests and web hooks.
- Mockafka - Python library designed for mocking Kafka in a testing environment. It simplifies testing Kafka-integrated applications by providing an in-memory mock for aiokafka and confluent-kafka-python.
- mocket - A socket mock framework with gevent/asyncio/SSL support.
- Mockintosh - aims to provide usual HTTP mock service functionality with small resource footprint, making it friendly for microservice applications.
- Pretend - is a library to make stubbing with Python easier.
- responses - A utility library for mocking out the requests Python library.
- time-machine - Travel through time in your tests.
- trustme - gives you a fake certificate authority (CA) that you can use to generate fake TLS certs to use in your tests.
- doublex - Powerful test doubles framework for Python.
- D-MemFS - Zero-dependency in-memory virtual filesystem with hard quotas. Provides an explicit, isolated instance instead of patching global state.
- pyfakefs - A fake file system that mocks the Python file system modules.
- moto - allows you to easily mock out tests based on AWS infrastructure.
Mutation Testing
- Cosmic Ray - makes small changes to your source code, running your test suite for each one.
- Mutatest - Python mutation testing.
- Mutmut - is a mutation testing system for Python, with a strong focus on ease of use.
- MutPy - MutPy is a mutation testing tool for Python 3.x source code
- xmutant.py - Python 3.6 bytecode based mutation analysis framework
- bough - Bough is a polyglot incremental mutation tester.
Object Factories
- factory_boy - A test fixtures replacement for Python.
- mixer - Another fixtures replacement. Supports Django, Flask, SQLAlchemy, Peewee and etc.
- Model Bakery - offers you a smart way to create fixtures for testing in Django.
- polyfactory - A simple and powerful mock data generation library, based around type hints and supporting dataclasses, typed-dicts, pydantic models, msgspec structs and more.
Penetration Testing
- fsociety - A Modular Penetration Testing Framework.
- python-pentest-tools - Python tools for penetration testers.
- fencer - Fencer is an automated API security testing tool. It's an experimental project to see how much of the API security testing process can be automated.
Property Based Testing
- Atheris - is a coverage-guided Python fuzzing engine. It supports fuzzing of Python code, but also native extensions written for CPython.
- hypofuzz - Adaptive fuzzing of Hypothesis tests.
- Hypothesis - is an advanced Quickcheck style property based testing library.
- hypothesis-crosshair - Add the power of solver-based symbolic execution to your Hypothesis tests with CrossHair.
- icontract-hypothesis - Instead of writing manually the Hypothesis search strategies for a function, `icontract-hypothesis` infers them based on the function's precondition.
- hypothesis-auto - An extensions for Hypothesis that provides fully automatic testing for type hinted functions.
Reporting
- pytest-html-plus - Get a self-contained, actionable, easy-to-read single page HTML unified reports summarizing all your test results. Detect flaky tests.
Resources
- Articles
  - Anna-Lena Popkes: "Mocking in Python" - Mocking might seem confusing in the beginning but once you understand the basics it can be very helpful.
  - async test patterns for Pytest - Learn some handy async examples and patterns for testing in Pytest.
  - Getting Started With Property-Based Testing in Python With Hypothesis and Pytest
  - How not to footgun yourself when writing tests - a showcase of flaky tests
  - Stargirl Flowers: “My Python testing style guide” - An attempt to catalog some practices around testing Python projects. It's not meant to be treated as dogma.
  - Test & Code: Python Testing - Test & Code is a weekly podcast hosted by Brian Okken. The show covers a wide array of topics including software engineering, development, testing, Python programming, and many related topics.
  - Testing your Python Code with Hypothesis - A look at how Hypothesis can help you discover errors in your code.
  - Patterns of flakey Python tests - This post details several patterns that cause flakey Python tests. Being aware of these common causes can help when investigating your own flakey tests.
  - Unit testing Python code in Jupyter notebooks - This article covers several options for unit testing Python code in a Jupyter notebook.
  - 30 best practices for software development and testing - These software engineering rules and testing best practices might help save you time and headaches.
  - Stargirl Flowers: “My Python testing style guide” - An attempt to catalog some practices around testing Python projects. It's not meant to be treated as dogma.
  - Unit testing Python code in Jupyter notebooks - This article covers several options for unit testing Python code in a Jupyter notebook.
- Books

Programming Languages

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Data

Tools

Indexes

Applications

Experiments

Awesome

awesome-python-testing

Assertions

Behavior-driven Development

Code Coverage

Design by Contract

Fake Data

Fixtures

LLM & MCP Testing

Load Testing

Memory Management

Mock and Stub

Mutation Testing

Object Factories

Penetration Testing

Property Based Testing

Reporting

Resources

Articles

Books