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fast-check vs Hypothesis

fast-check

fast-check.dev

Hypothesis

hypothesis.works

Side by side

Figurefast-checkHypothesis
Price fromFree—
Free planYes—
Free trial——
Open sourceYes—
WebYes—
Windows—Yes
macOS—Yes
LinuxYesYes
APIYes—
Self-hostedYes—
Plans published10
Spec score · Property-Based Testing Tools8.9 (#1)7.4 (#15)
IntegrationsThe ecosystem lists official integrations for AVA, Jest, and Vitest, along with connectors for libraries such as Zod and io-ts.First-party extras include integrations for specific packages, and the documentation gives Django as an example installable extra.
What it doesfast-check is a property-based testing framework for JavaScript and TypeScript.Hypothesis is a property-based testing library for Python that generates inputs within ranges described by the user.

Plans

fast-check

  • fast-checkFree

fast-check pricing →

Hypothesis

No plans published.

Hypothesis pricing →

In their own words

fast-check

fast-check is a property-based testing library for JavaScript and TypeScript. It generates inputs and checks expected behavior across many cases, then reduces a failure to a smaller counterexample to help investigate it. Tests combine arbitraries that create values, properties that describe behavior, runners that execute checks, and plugins that extend runners. It works with Jest, Mocha, Vitest, Ava, Bun, Deno, the Node.js test runner, and other test runners. Runs can be configured with seeds, generated-value sizes, time limits, and result reporting globally or per assertion. fast-check supports stateful testing and reproducible failures; its project also describes fuzzing and generative testing for issues such as race conditions and prototype poisoning. The project is open source under the MIT license, and the listed plan is free. Version 5.x requires Node.js 22.12.0 or later and ECMAScript 2025; TypeScript 5.8 or later is optional.

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Hypothesis

Hypothesis is a free property-based testing library for Python. You describe behavior that your code should satisfy, and it generates inputs—including edge cases—to explore whether those properties hold. Strategies let you define and combine the data used in tests. When a failure appears, Hypothesis reports an example and tries to simplify it; failing examples are stored in a local test database for replay in later runs. Tests run as ordinary Python functions within pytest, unittest, nose, or another framework that can run a Python function. Hypothesis is not itself a test runner. By default, it generates 100 random inputs per test, and max_examples controls that count. Rule-based state machines can generate sequences of operations for testing stateful systems and objects. The project supports and tests CPython and PyPy 3.10 and later, and says it regularly works on Linux, macOS, Windows, and Emscripten. Its compatibility policy supports only the newest Hypothesis version; undocumented behavior may change in patch releases.

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