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pytest-dev/pytest 9.1.1 — A test framework with fixtures and assert diagnostics

A rundown of the features of pytest, the Python test framework that offers fixtures, automatic test collection, and detailed information about failed asserts, along with its latest release 9.1.1.

Repositorypytest-dev/pytest ↗

A rundown of the features of pytest, the Python test framework that offers fixtures, automatic test collection, and detailed information about failed asserts, along with its latest release 9.1.1.

GitHub description: The pytest framework makes it easy to write small tests, yet scales to support complex functional testing

stars
★ 14.6K
Forks
3.5K
Language
Python
License
MIT
Latest release
9.1.1
Read original source ↗View on GitHub ↗

The latest release of pytest-dev/pytest is 9.1.1. It is a Python test framework that makes small tests easy to write while also taking on complex functional tests. It automatically finds and runs test modules and functions, and provides detailed information about failed asserts (assertions). What exactly 9.1.1 changed is not recorded in the available material.

This framework keeps writing tests, running them, and identifying the cause of failures in one place. Module-level fixtures (a way to define reusable setup data for tests at module scope) put common setup in a single spot, while parametrization (a way to run the same test repeatedly with multiple input values) lets you split the inputs and run them separately. Tests written with unittest can be slotted into the same flow and run alongside, and there is a plugin structure for adding external functionality. It supports Python 3.10+ or PyPy3 and is distributed as free open-source software under the MIT license. The repository was started on June 15, 2015 and has 14621 stars.

That said, the published record only supports so much. The README's feature description is a sentence about the supported scope rather than a measured performance, and it contains no performance figures or comparison data. This repository's momentum rating is 3.0 on a 0–10 scale, and the novelty, technology, and quality ratings are not listed. So the judgment that remains is this: it is a familiar tool that has been around since 2015 rather than a newly surfaced project, which means that when picking a testing tool for a Python project it is still close to a safe default.