The property-based testing library for Python
Hegel
A universal property-based testing protocol and family of libraries, built on Hypothesis
Hypothesis, Antithesis, synthesis
Introducing Hegel, our new family of property-based testing libraries.
Choosing properties for property-based testing
Or, I want to use PBT, but I can never think of any properties to use
Antithesis: autonomous software testing
Try the Antithesis autonomous testing platform and find bugs in your software with perfect reproducibility
Property Testing with Agent Swarms
Using agents to build property-testing machinery, then turn the findings into small, reviewable fixes.
Readable Robot Tests: Bridge the Tech Gap
A walkthrough of the Robot Testing pattern in Flutter, an end-to-end technique that keeps tests readable for non-technical stakeholders and reusable as code grows.
Structure and Interpretation of Test Cases • Kevlin Henney • GOTO 2022
A universal approach to mocking
Defunctionalise your continuations and your tests can run any computation a step at a time.
Test-Driven Approaches to Software Engineering with Large Language...
Tests increasingly participate in the decisions made by large language models and software engineering agents. They specify intended behavior, guide program construction and repair, select...
SciPy 1.0: fundamental algorithms for scientific computing in Python
SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
Testing functional UIs - Hayleigh Thompson | Lambda Days 2025
Testing can be fun, actually
Writing and maintaining tests is boring. But they're also some of the most valuable code we can write. With this blog post you'll learn a criminally underrated testing technique to add to your testing toolbox that can make tests a whole lot more pleasant.
Pydantic Monty: A Minimal Python Sandbox for AI Agents | Pydantic
Meet Monty — a secure, minimal Python interpreter written in Rust for running AI-generated code. Learn where it sits between tool calling and full computer use, and why CodeMode matters for AI agents.
