hypothesis library

hypothesis 6.100.1

pip install hypothesis Copy PIP instructions

Released: Apr 8, 2024

A library for property-based testing

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License: Mozilla Public License 2.0 (MPL 2.0) (MPL-2.0)

Author: David R. MacIver and Zac Hatfield-Dodds

Tags python, testing, fuzzing, property-based-testing

Requires: Python >=3.8

Classifiers

• 5 - Production/Stable
• OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)
• Microsoft :: Windows
• Python :: 3
• Python :: 3 :: Only
• Python :: 3.8
• Python :: 3.9
• Python :: 3.10
• Python :: 3.11
• Python :: 3.12
• Python :: Implementation :: CPython
• Python :: Implementation :: PyPy
• Education :: Testing
• Software Development :: Testing

Project description

Hypothesis is an advanced testing library for Python. It lets you write tests which are parametrized by a source of examples, and then generates simple and comprehensible examples that make your tests fail. This lets you find more bugs in your code with less work.

Hypothesis is extremely practical and advances the state of the art of unit testing by some way. It’s easy to use, stable, and powerful. If you’re not using Hypothesis to test your project then you’re missing out.

Quick Start/Installation

If you just want to get started:

Links of interest

The main Hypothesis site is at hypothesis.works , and contains a lot of good introductory and explanatory material.

Extensive documentation and examples of usage are available at readthedocs .

If you want to talk to people about using Hypothesis, we have both an IRC channel and a mailing list .

If you want to receive occasional updates about Hypothesis, including useful tips and tricks, there’s a TinyLetter mailing list to sign up for them .

If you want to contribute to Hypothesis, instructions are here .

If you want to hear from people who are already using Hypothesis, some of them have written about it .

If you want to create a downstream package of Hypothesis, please read these guidelines for packagers .

Project details

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• Notifications

Releases: HypothesisWorks/hypothesis

Hypothesis for python - version 6.74.0.

This release adds support for nullable pandas dtypes in "pandas()" (issue #3604 ). Thanks to Cheuk Ting Ho for implementing this at the PyCon sprints!

The canonical version of these notes (with links) is on readthedocs.

Hypothesis for Python - version 6.72.4

This patch fixes type annotations for the "arrays()" strategy. Thanks to Francesc Elies for pull request #3602 .

Hypothesis for Python - version 6.72.3

This patch fixes a bug with "from_type()" with "dict[tuple[int, int], str]" (issue #3527 ).

Thanks to Nick Muoh at the PyCon Sprints!

Hypothesis for Python - version 6.72.2

This patch refactors our internals to facilitate an upcoming feature.

Hypothesis for Python - version 6.72.1

This patch fixes some documentation and prepares for future features.

Hypothesis for Python - version 6.72.0

This release deprecates "Healthcheck.all()", and adds a codemod to automatically replace it with "list(Healthcheck)" (issue #3596 ).

Hypothesis for Python - version 6.71.0

This release adds "GitHubArtifactDatabase", a new database backend that allows developers to access the examples found by a Github Actions CI job. This is particularly useful for workflows that involve continuous fuzzing, like HypoFuzz.

Thanks to Agustín Covarrubias for this feature!

• 👍 1 reaction

Hypothesis for Python - version 6.70.2

This patch clarifies the reporting of time spent generating data. A simple arithmetic mean of the percentage of time spent can be misleading; reporting the actual time spent avoids misunderstandings.

Thanks to Andrea Reina for reporting and fixing issue #3598 !

Hypothesis for Python - version 6.70.1

This patch updates our vendored list of top-level domains, which is used by the provisional "domains()" strategy.

Hypothesis for Python - version 6.70.0

This release adds an optional "domains=" parameter to the "emails()" strategy, and excludes the special-use .arpa domain from the default strategy (issue #3567 ).

Thanks to Jens Tröger for reporting and fixing this bug!

Test faster, fix more

Most testing is ineffective

Normal “automated” software testing is surprisingly manual. Every scenario the computer runs, someone had to write by hand. Hypothesis can fix this.

Hypothesis is a new generation of tools for automating your testing process. It combines human understanding of your problem domain with machine intelligence to improve the quality of your testing process while spending less time writing tests.

Don’t believe us? Here’s what some of our users have to say:

At Lyst we've used it in a wide variety of situations, from testing APIs to machine learning algorithms and in all cases it's given us a great deal more confidence in that code. Alex Stapleton , Lead Backend Engineer at Lyst

When it comes to validating the correctness of your tools, nothing comes close to the thoroughness and power of Hypothesis. Cory Benfield , Open source Python developer

Hypothesis has been brilliant for expanding the coverage of our test cases, and also for making them much easier to read and understand, so we’re sure we’re testing the things we want in the way we want. Kristian Glass , Director of Technology at LaterPay

Hypothesis has located real defects in our code which went undetected by traditional test cases, simply because Hypothesis is more relentlessly devious about test case generation than us mere humans! Rob Smallshire , Sixty North

See more at our testimonials page .

What is Hypothesis?

Hypothesis is a modern implementation of property based testing , designed from the ground up for mainstream languages.

Hypothesis runs your tests against a much wider range of scenarios than a human tester could, finding edge cases in your code that you would otherwise have missed. It then turns them into simple and easy to understand failures that save you time and money compared to fixing them if they slipped through the cracks and a user had run into them instead.

Hypothesis currently has a fully featured open source Python implementation and a proof of concept Java implementation that we are looking for customers to partner with to turn into a finished project. Plans for C and C++ support are also in the works.

How do I use it?

Hypothesis integrates into your normal testing workflow. Getting started is as simple as installing a library and writing some code using it - no new services to run, no new test runners to learn.

Right now only the Python version of Hypothesis is production ready. To get started with it, check out the documentation or read some of the introductory articles here on this site .

Once you’ve got started, or if you have a large number of people who want to get started all at once, you may wish to engage our training services .

If you still want to know more, sign up to our newsletter to get an email every 1-2 weeks about the latest and greatest Hypothesis developments and how to test your software better.

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Find your best test ideas

Hypothesis library is currently in beta, hypothesis library.