hypothesis
z3
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hypothesis | z3 | |
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20 | 28 | |
7,275 | 9,731 | |
1.5% | 1.7% | |
9.9 | 9.8 | |
3 days ago | 1 day ago | |
Python | C++ | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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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
- 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
z3
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Ask HN: What is the current state of "logical" AI?
See https://cacm.acm.org/magazines/2023/6/273222-the-silent-revo... and also modern production rules engines like https://drools.org/
Oddly, back when “expert system shells” were cool people thought 10,000 rules were difficult to handle, now 1,000,000 might not be a problem at all. Back then the RETE algorithm was still under development and people were using linear search and not hash tables to do their lookups.
Also https://github.com/Z3Prover/z3
Note “the semantic web” is both an advance and a retreat in that OWL is a subset of first order logic which is really decidable and sorta kinda fast. It can do a lot but people aren’t really happy with what it can do.
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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
[4] https://github.com/dafny-lang/dafny
[5] https://github.com/dotnet/csharplang/issues/105
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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.
[0]https://github.com/Z3Prover/z3
- Modern SAT solvers: fast, neat and underused (2018)
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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.
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Constraint Programming 'linking' variables
Z3 theorem prover SMT solver might help you.
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General mathematical expression analysis system
Other than that, you should look at Z3 which is pretty damn good at these sort of theorems/constraints.
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-🎄- 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
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Question about Predicate Transformer Semantics
I'm trying to learn a little bit about Predicate Transformer Semantics (PTS) as part of a quick exploration of Z3.
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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.
https://github.com/Z3Prover/z3
What are some alternatives?
pytest - The pytest framework makes it easy to write small tests, yet scales to support complex functional testing
employee-scheduling-ui - An UI component for Employee Scheduling application.
Robot Framework - Generic automation framework for acceptance testing and RPA
advent-of-code - My solutions to http://adventofcode.com/ :)
Behave - BDD, Python style.
advent-of-code-go - All 8 years of adventofcode.com solutions in Go/Golang; 2015 2016 2017 2018 2019 2020 2021 2022
nose2 - The successor to nose, based on unittest2
magmide - A dependently-typed proof language intended to make provably correct bare metal code possible for working software engineers.
nose - nose is nicer testing for python
ikos - Static analyzer for C/C++ based on the theory of Abstract Interpretation.
Schemathesis - Automate your API Testing: catch crashes, validate specs, and save time
androguard - Reverse engineering and pentesting for Android applications