Repository History
17 repositories tagged with testing
aimock: Comprehensive Mocking for AI Application Testing
aimock is a powerful tool designed for comprehensive testing of AI applications by mocking various AI APIs and services. It offers a unified solution to simulate interactions with LLM APIs, vector databases, and other AI infrastructure, ensuring deterministic and efficient testing workflows. With features like record and replay, chaos testing, and seamless framework integrations, aimock significantly simplifies the development and validation of robust AI systems.
Green: A Clean, Colorful, and Fast Python Test Runner
Green is an innovative Python test runner designed for clarity, speed, and visual appeal. It provides a clean, colorful, and fast way to execute `unittest` based tests, enhancing the developer experience with detailed, aligned output and parallel execution.
httmock: A Powerful Mocking Library for Python Requests
httmock is an essential Python library designed for mocking HTTP requests made by the popular `requests` library. It allows developers to easily simulate API responses, making it ideal for testing applications that interact with external services. With httmock, you can control network interactions, ensuring reliable and repeatable tests without relying on actual network calls.
Mocket: A Comprehensive Socket Mocking Framework for Python
Mocket is a powerful Python framework designed for monkey-patching the `socket` and `ssl` modules, enabling robust testing of network-dependent applications. It serves as both a low-level framework for building custom clients and a ready-to-use mock for HTTP/HTTPS calls, supporting various environments including asyncio and MicroPython. This tool simplifies the process of isolating and testing Python clients that communicate over the socket protocol.
vcrpy: Simplify and Speed Up Python HTTP Testing
vcrpy is a Python library that automatically mocks your HTTP interactions, making testing simpler and significantly faster. Inspired by Ruby's VCR, it records HTTP requests and responses to a 'cassette' file during the first test run, then replays them in subsequent runs, eliminating actual network traffic. This approach ensures deterministic tests, allows offline development, and boosts test execution speed.
Model Mommy: A Legacy Python Fixture Factory, Migrate to Model Bakery
Model Mommy was a popular Python library designed to simplify the creation of smart test fixtures and realistic test data. This project is no longer actively maintained, and its users are strongly advised to migrate to its successor, Model Bakery. The renaming to Model Bakery was a conscious decision to avoid reinforcing gender stereotypes within the technology community.
Faker: Generate Realistic Fake Data for Your Python Projects
Faker is a powerful Python package designed to generate realistic fake data. It's an essential tool for bootstrapping databases, creating test data, filling persistence layers for stress testing, or anonymizing sensitive production data. With support for various data types and localization, Faker streamlines development and testing workflows.
Mimesis: A Powerful Python Library for Realistic Fake Data Generation
Mimesis is a robust Python library designed for generating fake yet realistic data across various languages and locales. It simplifies the creation of diverse data types, from personal information to financial details. This makes it an invaluable tool for development, testing, and anonymization tasks.
EvalPlus: Rigorous Evaluation for LLM-Synthesized Code
EvalPlus is a robust framework designed for the rigorous evaluation of code generated by Large Language Models (LLMs). It extends standard benchmarks like HumanEval and MBPP with significantly more tests, offering precise assessment of code correctness and efficiency. This tool is crucial for developers and researchers aiming to thoroughly validate LLM-synthesized code.
Promptfoo: LLM Evaluation and Red Teaming for AI Applications
Promptfoo is an open-source CLI and library designed for evaluating and red-teaming Large Language Model (LLM) applications. It enables developers to test prompts, agents, and RAGs, compare model performance, and secure AI apps through vulnerability scanning. With simple declarative configs and CI/CD integration, Promptfoo helps ship reliable and secure AI solutions.
responses: Mocking Python Requests for Robust Testing
responses is a Python library designed to simplify the process of mocking the `requests` library during testing. It allows developers to define predictable HTTP responses, enabling isolated and reliable unit tests for applications that interact with external APIs. This utility is essential for ensuring test stability and speed by avoiding actual network calls.
Wake: A Python Framework for Secure Solidity Development and Fuzz Testing
Wake is a robust Python-based framework designed for secure Solidity development and comprehensive fuzz testing. It provides built-in vulnerability detectors, helping developers build more secure Ethereum dApps. With features like a VS Code extension and CI/CD integration, Wake streamlines the smart contract development workflow.