hypothesis
z3
Our great sponsors
hypothesis | z3 | |
---|---|---|
20 | 28 | |
7,225 | 9,627 | |
1.2% | 1.4% | |
9.9 | 9.9 | |
4 days ago | 5 days ago | |
Python | C++ | |
GNU General Public License v3.0 or later | 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.
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.
-
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
-
Top 5 decentralized app development frameworks
Unlike other frameworks mentioned in this article, Brownie’s test language is Python using hypothesis.
- What Is Property Based Testing?
-
Go: Fuzzing Is Beta Ready
People can have different definitions and still communicate usefully, and I think there is not 100% agreement on the exact boundaries between the two.
That said, for me: they are distinct but related, and that distinction is useful.
For example, Hypothesis is a popular property testing framework. The authors have more recently created HypoFuzz, which includes this sentence in the introduction:
“HypoFuzz runs your property-based test suite, using cutting-edge fuzzing techniques and coverage instrumentation to find even the rarest inputs which trigger an error.”
Being able to talk about fuzzing and property testing as distinct things seems useful — saying something like “We added fuzzing techniques to our property testing framework“ is more meaningful than “We added property testing techniques to our property testing framework“ ;-)
My personal hope is there will be more convergence, and work to add first-class fuzzing support in a popular language like Go will hopefully help move the primary use case for fuzzing to be about correctness, with security moving to an important but secondary use case.
z3
-
Lean4 helped Terence Tao discover a small bug in his recent paper
Code correctness is a lost art. I requirement to think in abstractions is what scares a lot of devs to avoid it. The higher abstraction language (formal specs) focus on a dedicated language to describe code, whereas lower abstractions (code contracts) basically replace validation logic with a better model.
C# once had Code Contracts[1]; a simple yet powerful way to make formal specifications. The contracts was checked at compile time using the Z3 SMT solver[2]. It was unfortunately deprecated after a few years[3] and once removed from the .NET Runtime it was declared dead.
The closest thing C# now have is probably Dafny[4] while the C# dev guys still try to figure out how to implement it directly in the language[5].
[1] https://www.microsoft.com/en-us/research/project/code-contra...
[2] https://github.com/Z3Prover/z3
[3] https://github.com/microsoft/CodeContracts
-
Programming Languages Going Above and Beyond
I believe, Nim also has this functionality, although, it uses the [0]Z3Prover tool with a nim frontend [1]"DrNim" for proving.
- Modern SAT solvers: fast, neat and underused (2018)
-
If You've Got Enough Money, It's All 'Lawful'
Don't get me wrong, there are times when Microsoft got it right the first time that was technically far superior to their competitors. Windows IOCP was theoretically capable of doing C10K as far back in 1994-95 when there wasn't any hardware support yet and UNIX world was bickering over how to do asynchronous I/O. Years later POSIX came up with select which was a shoddy little shit in comparison. Linux caved in finally only as recently as 2019 and implemented io_uring. Microsoft research has contributed some very interesting things to computer science like Z3 SAT solver and in collaboration with INRIA made languages like F* and Low* for formal specification and verification. But all this dwarfs in comparison to all the harm they did.
-
General mathematical expression analysis system
Other than that, you should look at Z3 which is pretty damn good at these sort of theorems/constraints.
-
-🎄- 2022 Day 21 Solutions -🎄-
In the end I used Z3 Julia bindings instead. The hardest part was to get the result back from it, because I kept running into assertion violations from inside Z3
-
The Little Prover
> And you propose me instead to go and reverse engineer library Js code which I am not that proficient in, and rewrite all code in Java instead?..
Yes, rather than demand others cater to your whims, frankly.
Do you realise how hypocritical it sounds to complain that you are not proficient in Javascript, when others might not be proficient in ?
Go use Z3 if you need a prover in C++ (or Java), its far more robust (provided its the type you're after) than someones 700 LoC JavaScript implementation.
- Ask HN: When you code at work, how do you code in your time off?
-
AMA: We are the creators of The Puzzler Hunt. Ask us anything!
My open-source project https://github.com/obijywk/grilops (excuse the shameless plug) can help when creating Nikoli-style grid logic puzzles, and we used it during the development of Ents, Resolution, and Missing Pieces (and to check uniqueness of Digital Gaming solutions). The constraint solver library it depends on, https://github.com/Z3Prover/z3 from Microsoft Research, is also very useful on its own, and we used it to help with the creation of Art Gallery and Global Shipping Crisis. Dennis Yurichev's SAT/SMT By Example is an extensive resource for learning how to use these kinds of tools to solve all sorts of problems, including puzzle solving.
- Make formal verification and provably correct software practical and mainstream
What are some alternatives?
pytest - The pytest framework makes it easy to write small tests, yet scales to support complex functional testing
Robot Framework - Generic automation framework for acceptance testing and RPA
Behave - BDD, Python style.
nose2 - The successor to nose, based on unittest2
nose - nose is nicer testing for python
Schemathesis - Automate your API Testing: catch crashes, validate specs, and save time
mamba - The definitive testing tool for Python. Born under the banner of Behavior Driven Development (BDD).
Slash - The Slash testing infrastructure
Python Testing Crawler - A crawler for automated functional testing of a web application
Selenium Wire - Extends Selenium's Python bindings to give you the ability to inspect requests made by the browser.
employee-scheduling-ui - An UI component for Employee Scheduling application.
RedExpect - Automate SSH in python easily!