recreational-rosette
alive2
| recreational-rosette | alive2 | |
|---|---|---|
| 1 | 5 | |
| 108 | 688 | |
| - | 3.1% | |
| 0.0 | 9.3 | |
| almost 6 years ago | 8 days ago | |
| Racket | C++ | |
| - | MIT License |
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.
recreational-rosette
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Programming in Z3 by learning to think like a compiler
I don't know about "practical," but you might enjoy this: https://github.com/kach/recreational-rosette
alive2
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CBMC: C bounded model checker. (2021)
Another problem with LLVM I’ve heard about is that it’s intermediate language or API or something is a moving, informally-specified target. People who know LLVM internals might weigh in on that claim. If true, it’s actually easier to target C or a subset of Rust just because it’s static and well-understood.
Two projects sought to mitigate these issues by going in different directions. One was a compiler backend that aimed to be easy to learn with well-specified IL. The other aimed to formalize LLVM’s IL.
http://c9x.me/compile/
https://github.com/AliveToolkit/alive2
There have also been typed, assembly languages to support verification from groups like FLINT. One can also compile language-specific analysis with a certified to LLVM IL compiler. Integrating pieces from different languages can have risks. That (IIRC) is being mitigated by people doing secure, abstract compilation.
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Basic SAT model of x86 instructions using Z3, autogenerated from Intel docs
You can use it to (mostly) validate small snippets are the same. See Alive2 for the application of Z3/formalization of programs as SMT for that [1]. As far as I'm aware there are some problems scaling up to arbitrarily sized programs due to a lack of formalization in higher level languages in addition to computational constraints. With a lot of time and effort it can be done though [2].
1. https://github.com/AliveToolkit/alive2
2. https://sel4.systems/
- John Regehr: Alive2 LLVM optims verification
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Verifying GCC optimizations using an SMT solver
Yeah, this kind of thing is nice.
Alive had been used for years (almost a decade actually) by people to verify LLVM instcombine transforms.
Alive2 (https://github.com/AliveToolkit/alive2) makes it easier to do the same with most optimization passes.
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Programming in Z3 by learning to think like a compiler
Alive/Alive2 [1] is one of the most famous frameworks for compiler transformation verification using BitVec logic
[1] https://github.com/AliveToolkit/alive2
What are some alternatives?
angr - A powerful and user-friendly binary analysis platform!
CrossHair - An analysis tool for Python that blurs the line between testing and type systems.
zz - 🍺🐙 ZetZ a zymbolic verifier and tranzpiler to bare metal C
klee - KLEE Symbolic Execution Engine
Symbolica - Symbolica's open-source symbolic execution engine. [Moved to: https://github.com/Symbolica/Symbolica]
llvm-tutor - A collection of out-of-tree LLVM passes for teaching and learning
Cassius - A CSS specification and reasoning engine
sprdpl - Simple Python Recursive-Descent Parsing Library
taxoptimizer
coq - Coq is a formal proof management system. It provides a formal language to write mathematical definitions, executable algorithms and theorems together with an environment for semi-interactive development of machine-checked proofs.