hypothesis
Deal
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hypothesis | Deal | |
---|---|---|
20 | 9 | |
7,254 | 690 | |
1.1% | 3.5% | |
9.9 | 4.7 | |
10 days ago | 25 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | MIT License |
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hypothesis
- Hypothesis
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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:
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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.
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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?
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Python toolkits
Hypothesis to generate dummy data for test.
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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.
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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.
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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... .
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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
Deal
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What features would you want in a new programming language?
I started using a design by contract library for a Python project this year and it made my code safer and easier to use.
- deal: Design by contract for Python. Write bug-free code. Add a few decorators, get static analysis and tests for free.
- GitHub - life4/deal: Design by contract for Python. Write bug-free code. Add a few decorators, get static analysis and tests for free.
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Design By Contract
Have you used Design by Contract in Python projects before? I know that it isn't a first class feature of the language but I see some libraries out there (this one seems potentially promising). Just wondering what the pros and cons are when it comes to gluing DBC onto Python and if anyone can give a yea or nay to it.
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Make tests a part of your app
deal is a library for Design-by-Contract.
What are some alternatives?
pytest - The pytest framework makes it easy to write small tests, yet scales to support complex functional testing
returns - Make your functions return something meaningful, typed, and safe!
Robot Framework - Generic automation framework for acceptance testing and RPA
Toolz - A functional standard library for Python.
Behave - BDD, Python style.
Pyrsistent - Persistent/Immutable/Functional data structures for Python
nose2 - The successor to nose, based on unittest2
funcy - A fancy and practical functional tools
nose - nose is nicer testing for python
classes - Smart, pythonic, ad-hoc, typed polymorphism for Python
Schemathesis - Automate your API Testing: catch crashes, validate specs, and save time
fn.py - Functional programming in Python: implementation of missing features to enjoy FP