hypothesis VS fast-check

Hypothesis for Property-Based Testing: Hypothesis is a Python library facilitating property-based testing. It offers a distinct advantage by generating a wide array of input data based on specified properties or invariants within the code. The perks of Hypothesis include:

But then add tests! Tests for LaTeX equations that had never been executable as code.

https://github.com/HypothesisWorks/hypothesis :

> Hypothesis is a family of testing libraries which let you write tests parametrized by a source of examples. A Hypothesis implementation then generates simple and comprehensible examples that make your tests fail. This simplifies writing your tests and makes them more powerful at the same time, by letting software automate the boring bits and do them to a higher standard than a human would, freeing you to focus on the higher level test logic.

> This sort of testing is often called "property-based testing", and the most widely known implementation of the concept is the Haskell library QuickCheck, but Hypothesis differs significantly from QuickCheck and is designed to fit idiomatically and easily into existing styles of testing that you are used to, with absolutely no familiarity with Haskell or functional programming needed.

pgregory.net/rapid is a modern Go property-based testing library initially inspired by the power and convenience of Python's Hypothesis.

Hypothesis to generate dummy data for test.

To create your own test cases, I recommend you use hypothesis-graphql in combination with hypothesis. hypothesis is a property-based testing library. Property-based testing is an approach to testing in which you make assertions about the result of a test given certain conditions and parameters. For example, if you have a mutation that requires a boolean parameter, you can assert that the client will receive an error if it sends a different type. hypothesis-graphql is a GraphQL testing library that knows how to use hypothesis strategies to generate query documents.

The Hypothesis stateful testing code is somewhat self-contained, since it mostly builds on top of internal APIs that already existed.

We've had a lot of success combining that approach with property-based testing (https://github.com/HypothesisWorks/hypothesis) for the query engine at backtrace: https://engineering.backtrace.io/2020-03-11-how-hard-is-it-t... .

Systems I've used for this include https://agda.readthedocs.io/en/v2.6.0.1/getting-started/what... https://coq.inria.fr https://www.idris-lang.org and https://isabelle.in.tum.de

An easier alternative is to try disproving the statement, by executing it on thousands of examples and seeing if any fail. That gives us less confidence than a full proof, but can still be better than traditional "there exists" tests. This is called property checking or property-based testing. Systems I've used for this include https://hypothesis.works https://hackage.haskell.org/package/QuickCheck https://scalacheck.org and https://jsverify.github.io

Libraries like JSVerify or Fast-Check offer essential tools to facilitate property-based testing.

Strong agree!

For JavaScript, I suggest folks check out fast-check [0] and this introduction to property-based testing that uses fast-check [1].

This is broadly useful, but one specific place I've found it helpful was to check redux reducers against generated lists of actions to find unchecked edge cases and data assumptions.

[0] https://github.com/dubzzz/fast-check

Brilliant, this is a massive improvement. Previously I was fumbling around in https://github.com/dubzzz/fast-check/tree/main/packages/fast-check/documentation for info.

There's a library for exactly that: FastCheck.

It makes @fast-check/jest, the best option to integrate Jest and fast-check, as it provides an abstraction over both to ease their mutual integration.

The closest that I know of (and I have not used this) is zod-fast-check. It generates fast-check “arbitraries” (test data generators) for property-based testing based on zod schemas. Of course, this requires that you use zod to define your types, which has some downsides. Fortunately there is another tool, ts-to-zod, (which I also have not used) which will codegen zod schemas based on TS type definitions. If you thread these four libraries together you should end up with the ability to write random tests on generated data with very little overhead. In theory.

For something like this, I might break out fast-check for testing. It’s good at generating a wide range of values for a given type, and could help you get good test coverage without having to hand-author a lot of repetitive error inputs.