dafny
rust
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dafny | rust | |
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30 | 9 | |
2,665 | 4,984 | |
1.3% | 2.2% | |
9.7 | 5.2 | |
about 7 hours ago | 5 months ago | |
C# | Rust | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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dafny
- Candy – a minimalistic functional programming language
- Dafny – a verification-aware programming language
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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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The Deep Link Equating Math Proofs and Computer Programs
I don't think something that specific exists. There are a very large number of formal methods tools, each with different specialties / domains.
For verification with proof assistants, [Software Foundations](https://softwarefoundations.cis.upenn.edu/) and [Concrete Semantics](http://concrete-semantics.org/) are both solid.
For verification via model checking, you can check out [Learn TLA+](https://learntla.com/), and the more theoretical [Specifying Systems](https://lamport.azurewebsites.net/tla/book-02-08-08.pdf).
For more theory, check out [Formal Reasoning About Programs](http://adam.chlipala.net/frap/).
And for general projects look at [F*](https://www.fstar-lang.org/) and [Dafny](https://dafny.org/).
- Dafny
- The Dafny Programming and Verification Language
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In Which I Claim Rich Hickey Is Wrong
Dafny and Whiley are two examples with explicit verification support. Idris and other dependently typed languages should all be rich enough to express the required predicate but might not necessarily be able to accept a reasonable implementation as proof. Isabelle, Lean, Coq, and other theorem provers definitely can express the capability but aren't going to churn out much in the way of executable programs; they're more useful to guide an implementation in a more practical functional language but then the proof is separated from the implementation, and you could also use tools like TLA+.
https://dafny.org/
https://whiley.org/
https://www.idris-lang.org/
https://isabelle.in.tum.de/
https://leanprover.github.io/
https://coq.inria.fr/
http://lamport.azurewebsites.net/tla/tla.html
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Programming Languages Going Above and Beyond
> I think we can assume it won't be as efficient has hand written code
Actually, surprisingly, not necessarily the case!
If you'll refer to the discussion in https://github.com/dafny-lang/dafny/issues/601 and in https://github.com/dafny-lang/dafny/issues/547, Dafny can statically prove that certain compiler branches are not possible and will never be taken (such as out-of-bounds on index access, logical assumptions about whether a value is greater than or less than some other value, etc). This lets you code in the assumptions (__assume in C++ or unreachable_unchecked() under rust) that will allow the compiler to optimize the codegen using this information.
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What are the current hot topics in type theory and static analysis?
Most of the proof assistants out there: Lean, Coq, Dafny, Isabelle, F*, Idris 2, and Agda. And the main concepts are dependent types, Homotopy Type Theory AKA HoTT, and Category Theory. Warning: HoTT and Category Theory are really dense, you're going to really need to research them.
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What I've Learned About Formal Methods in Half a Year
I'm not sure if the author is here, or if my comment attempt was successful. So, can I suggest you take a look at a third leg of the formal methods stool?
If you are familiar with C, check out Frama-C (https://frama-c.com/) and the WP and RTE plugins. The approach is based on Tony Hoare and EWD's axiomatic semantics (https://en.wikipedia.org/wiki/Hoare_logic). It does not have a good memory management story, as far as I know, but is very good for demonstrating value correctness (RTE automatically generates assertions for numeric runtime errors, for example) and many memory errors.
If you are familiar with Ada, check out SPARK (https://www.adacore.com/about-spark), which is similar to Frama-C but has a much better interface in the AdaCore GNAT toolkit and IDE.
Both work similarly: Assertions in normal Ada or C code as well as the code itself are translated into SMT statements and fed to a SMT solver to find counterexamples---errors.
I have some blog posts from several years ago about Frama-C:https://maniagnosis.crsr.net/tags/applied%20formal%20logic.h... (And I really should get back into it; it's a lot of fun.)
If you are not familiar with Ada or C, Dafny (https://dafny.org/) is another option based on .NET and devoleped at Microsoft. It seems nigh-on perfect for this approach. (The language uses a garbage collector.) At the time I was looking, there was little documentation on Dafny, but that seems to have improved.
rust
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Have you ever wanted a library to check for 69 in a string?
You can use Tensorflow for Rust to simplify that task and avoid pain with regex. Just have the right mindset.
- Rust vs cpp for a new engineer to autonomous vehicles and robotics
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Making a better Tensorflow thanks to strong typing
What is the benefit of this compared to using bindings/a wrapper to Tensorflow, or other ML libraries written in C/C++, such as this community hosted project on tensorflow's github. If it's just for fun that is a valid enough reason imo, just curious since you describe it as a better Tensorflow because of the typing vs using the python wrapper, when there already exist ways to interact with tensorflow with both Rust and other statically typed languages, also including C++ (officially supported), C#, Haskell and Scala, as well as probably having bindings not mentioned on the documentation for more niche languages.
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Integrating machine learning models into Rust applications?
(3) You could use TensorFlow as your executor: https://github.com/tensorflow/rust
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Why Static Languages Suffer From Complexity
TensorFlow has language support for TypeScript well as Rust.
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Is PyO3 library production ready?
Thank you for the restponse! With tensorflow I am probably better of with something like; [tensorflow rust bindings](https://github.com/tensorflow/rust/tree/master/src). But I believe some useful extensions are still written in python for example; [TFDV](https://github.com/tensorflow/data-validation).. and how about scikit-learn or even something that is simpler like fb-prophet that is entirely written in python?
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How mature is the QT integration?
Tensorflow bindings exist, technically, but they're in a pretty rough state AFAIK.
- Feasibility of Using a Python Image Super Resolution Library in My Rust App
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Rusticles #10 - Wed Sep 09 2020
tensorflow/rust (Rust): Rust language bindings for TensorFlow
What are some alternatives?
tlaplus - TLC is a model checker for specifications written in TLA+. The TLA+Toolbox is an IDE for TLA+.
zig - General-purpose programming language and toolchain for maintaining robust, optimal, and reusable software.
FStar - A Proof-oriented Programming Language
leaf - Open Machine Intelligence Framework for Hackers. (GPU/CPU)
rust - Rust for the xtensa architecture. Built in targets for the ESP32 and ESP8266
anyhow - Flexible concrete Error type built on std::error::Error
koka - Koka language compiler and interpreter
Rustup - The Rust toolchain installer
Rust-for-Linux - Adding support for the Rust language to the Linux kernel.
rusty-machine - Machine Learning library for Rust
interactive - .NET Interactive combines the power of .NET with many other languages to create notebooks, REPLs, and embedded coding experiences. Share code, explore data, write, and learn across your apps in ways you couldn't before.
solana - Web-Scale Blockchain for fast, secure, scalable, decentralized apps and marketplaces.