hypothesis

The Hypothesis explain phase [1][2] does this! fails_on_empty_third_arg( a = "", # or any other generated value b = "", # or any other generated value c = "", d = "", # or any other generated value ) [1] https://hypothesis.readthedocs.io/en/latest/reference/api.ht... [2] https://github.com/HypothesisWorks/hypothesis/pull/3555

I have used Python's `hypothesis` as well, and I wish it were better. We had to rip it out at work as we were running into too many issues. I have also used Haskell's `QuickCheck` and Clojure's `spec` / `test.check` and have had a great experience with these. In my experience they "just work". Conversely, if you're trying to generate non-trivial datasets, you will likely run into situations where your specification is correct but Hypothesis' implementation fails to generate data, or takes an unreasonable amount of time to generate data. Example: Generate a 100x25 array of numeric values, where the only condition is that they must not all be zero simultaneously. [1] [1] https://github.com/HypothesisWorks/hypothesis/issues/3493

The python package Hypothesis[0] already does a great job bringing property-based testing to the people! I've used it and it's extremely powerful. [0]: https://github.com/HypothesisWorks/hypothesis

Anecdotally, Hypothesis was very far from "just working" for me. I don't think it's really production-ready, and that seems to be by design.[0] I did have quite a bit of prior experience with clojure.spec.alpha (with or without test.check), so despite some differences it's not like I was completely alien to the general ideas. [0] https://github.com/HypothesisWorks/hypothesis/issues/3493

Anecdotally, I had a fantastic experience with `clojure.spec.alpha` (with or without `test.check`), and when I went to use python's `hypothesis` it was just... abysmal. It seems like Hypothesis is unable to handle simple but "large" data sets >>by design<<, where "large" is really not so large. [0] It was such a pain that we ripped out Hypothesis (and generative testing altogether, sadly) completely from our python test suite at work. [0] https://github.com/HypothesisWorks/hypothesis/issues/3493

But then add tests! Tests for LaTeX equations that had never been executable as code. There are a number of ways to generate tests for functions and methods with and without parameter and return types. Property-based testing is one way to auto-generate test cases. Property testing: https://en.wikipedia.org/wiki/Property_testing awesome-python-testing#property-based-testing: https://github.com/cleder/awesome-python-testing#property-ba... 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

Author note: The original title is "Property based testing: tale of two shrinkers", which is retroactively a bit bad title choice on my part; the title I submitted it under sums the content up better I believe. This is a talk I gave internally at my company (I'm also planning to give an updated version at [Haskell Exchange 2022](https://events.skillsmatter.com/haskellx2022) in a month or so), about approaches to shrinking in PBT libraries and their issues, and how the [Hypothesis](https://github.com/HypothesisWorks/hypothesis) approach ("internal shrinking") solves those issues yet remains mostly unknown. There's also a bit of a call to action to PBT library maintainers to adopt this approach at the end :) I'm a co-maintainer of [elm-test](https://github.com/elm-explorations/test), the Elm PBT library, which has historically used the rose

We've had a lot of success combining that approach (cffi instead of ctypes) 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... .

The article separates types from property testing, I can sympathize, it is true, it makes sense. But testing is a tool for reliability to be used in conjunction with so many other tools like, you guessed it, typing. In 2016 when this article was written, typing wasn't yet popular in Python. But now it is, and indeed more popular than property testing will ever be. But Hypothesis doesn't support typing very well, though they do attempt [0]. But just take a look at that list of caveats. It is a hack job in comparison. Even if you don't generate your Strategies from types, Hypothesis requires you to know whether the type you construct is internally a sum type or a product type. [1] The testing framework shouldn't have to know these details, this is unnecessary complexity. If you think the QuickCheck strategy of having lawless unnamed instanc

You can already do that with a family of predicates if you write preconditions in your Python code (see my previous comment [1]). There is an ongoing discussion how to bring this in into Hypothesis (see the issue [2]). [1]: https://news.ycombinator.com/item?id=26018386 [2]: https://github.com/HypothesisWorks/hypothesis/issues/2701 edit: newlines

Hypohesis is a tool to do this https://github.com/HypothesisWorks/hypothesis/tree/master/hy...

The inputs shouldn’t be “random”, but the idea is that you should provide an input distribution that’s roughly representative of your expected distribution of real-world values (+ some perturbation to find edge cases). As for frameworks in the languages you listed... Ruby and Python: https://github.com/HypothesisWorks/hypothesis Elixir and Erlang: https://github.com/proper-testing/proper Node and JS: https://jsverify.github.io/ As for real world use-cases, imagine you’re writing a program that accepts timestamps as input and has to implement branching, requirements-defined business logic based off of them. When you’re writing your unit tests you can use the requirements to select timestamps that are “known good” and “known bad”, but it’s hard to explore this state space on your own. Same thing goes for handling unexpected inputs to certai

> lack of typing information isn't "free solo" hard but it certainly makes life a lot less pleasant. One of the projects I work on has switched to full type hinting along with heavy mypy¹ usage, and it has become an absolute pleasure to work with. Along with hypothesis², I can't recommend mypy enough. It must be said that retrofitting either to an existing project is a lot of work though. 1. http://www.mypy-lang.org/ 2. https://github.com/HypothesisWorks/hypothesis

"While Python is usually great for programming puzzles, this step in particular is crying out for something like QuickCheck: without it, we'll have to roll our own." Instead of rolling his own, the author could have just used Python's Hypothesis package.[1][2] [1] - https://github.com/HypothesisWorks/hypothesis [2] - https://pypi.org/project/hypothesis/