jsverify
hypothesis
jsverify | hypothesis | |
---|---|---|
5 | 20 | |
1,666 | 7,289 | |
0.1% | 0.9% | |
1.8 | 9.9 | |
about 3 years ago | 4 days ago | |
JavaScript | Python | |
MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
jsverify
-
The 5 principles of Unit Testing
Libraries like JSVerify or Fast-Check offer essential tools to facilitate property-based testing.
-
Ask HN: What's your favorite software testing framework and why?
I tend to use anything that offers property-testing, since tests are much shorter to write and uncover lots more hidden assumptions.
My go-to choices per language are:
- Python: Hypothesis https://hypothesis.readthedocs.io/en/latest (also compatible with PyTest)
- Scala: ScalaCheck https://scalacheck.org (also compatible with ScalaTest)
- Javascript/Typescript: JSVerify https://jsverify.github.io
- Haskell: LazySmallCheck2012 https://github.com/UoYCS-plasma/LazySmallCheck2012/blob/mast...
- When I wrote PHP (over a decade ago) there was no decent property-based test framework, so I cobbled one together https://github.com/Warbo/php-easycheck
All of the above use the same basic setup: tests can make universally-quantified statements (e.g. "for all (x: Int), foo(x) == foo(foo(x))"), then the framework checks that statement for a bunch of different inputs.
Most property-checking frameworks generate data randomly (with more or less sophistication). The Haskell ecosystem is more interesting:
- QuickCheck was one of the first property-testing frameworks, using random genrators.
- SmallCheck came later, which enumerates data instead (e.g. testing a Float might use 0, 1, -1, 2, -2, 0.5, -0.5, etc.). That's cute, but QuickCheck tends to exercise more code paths with each input.
- LazySmallCheck builds up test data on-demand, using Haskell's pervasive laziness. Tests are run with an error as input: if they pass, we're done; if they fail, we're done; if they trigger the error, they're run again with slightly more-defined inputs. For example, if the input is supposed to be a list, we try again with the two forms of list: empty and "cons" (the arguments to cons are both errors, to begin with). This exercises even more code paths for each input.
- LazySmallCheck2012 is a more versatile "update" to LazySmallCheck; in particular, it's able to generate functions.
-
Property Based Testing Framework for Node
The usage of hypothesis is very intuitive and simple, and presents the concept of property-based testing perfectly. So I also wanted to find an equivalent alternative in Node. Two of them have high star ratings on Github, JSVerify with 1.6K stars and fast-check with 2.8K stars. So I took some time to study fast-check a little bit and try to get closer to my daily work.
-
Machine Readable Specifications at Scale
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
-
React to Elm Migration Guide
Using create-react-app, you’ll run npm test which uses Jest internally. If you are dealing with a lot of data on the UI, or using TypeScript, use JSVerify for property tests. For end to end tests, Cypress is a great choice.
hypothesis
- Hypothesis
-
A Tale of Two Kitchens - Hypermodernizing Your Python Code Base
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:
-
Pix2tex: Using a ViT to convert images of equations into LaTeX code
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 v1.0.0, modern Go property-based testing library
pgregory.net/rapid is a modern Go property-based testing library initially inspired by the power and convenience of Python's Hypothesis.
- Was muss man als nicht-technischer Quereinsteiger in Data Science *wirklich* können?
-
Python toolkits
Hypothesis to generate dummy data for test.
-
Best way to test GraphQL API using Python?
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.
-
Fuzzcheck (a structure-aware Rust fuzzer)
The Hypothesis stateful testing code is somewhat self-contained, since it mostly builds on top of internal APIs that already existed.
-
Running C unit tests with pytest
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... .
-
Machine Readable Specifications at Scale
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
What are some alternatives?
greenlight - Clojure integration testing framework
pytest - The pytest framework makes it easy to write small tests, yet scales to support complex functional testing
testy - test helpers for more meaningful, readable, and fluent tests
Robot Framework - Generic automation framework for acceptance testing and RPA
LazySmallCheck2012 - Lazy SmallCheck with functional values and existentials!
Behave - BDD, Python style.
fast-check - Property based testing framework for JavaScript (like QuickCheck) written in TypeScript
nose2 - The successor to nose, based on unittest2
hitchstory - Type-safe YAML integration tests. Tests that write your docs. Tests that rewrite themselves.
nose - nose is nicer testing for python
datadriven - Data-Driven Testing for Go
Schemathesis - Automate your API Testing: catch crashes, validate specs, and save time