CrossHair
alive2
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CrossHair | alive2 | |
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8 | 4 | |
944 | 671 | |
- | 3.1% | |
9.2 | 9.3 | |
25 days ago | 8 days ago | |
Python | C++ | |
GNU General Public License v3.0 or later | 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.
CrossHair
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Try CrossHair while working other Python projects
Writing some Python for Hacktoberfest? Try out CrossHair while you do that and get credit for a blog post too! https://github.com/pschanely/CrossHair/issues/173
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What are some amazing, great python external modules, libraries to explore?
CrossHair, Hypothesis, and Mutmut for advanced testing.
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Formal Verification Methods in industry
When you say "formal verification methods", what kind of techniques are you interested in? While using interactive theorem provers will most likely not become very widespread, there are plenty of tools that use formal techniques to give more correctness guarantees. These tools might give some guarantees, but do not guarantee complete functional correctness. WireGuard (VPN tunnel) is I think a very interesting application where they verified the protocol. There are also some tools in use, e.g. Mythril and CrossHair, that focus on detecting bugs using symbolic execution. There's also INFER from Facebook/Meta which tries to verify memory safety automatically. The following GitHub repo might also interest you, it lists some companies that use formal methods: practical-fm
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Klara: Python automatic test generations and static analysis library
The main difference that Klara bring to the table, compared to similar tool like pynguin and Crosshair is that the analysis is entirely static, meaning that no user code will be executed, and you can easily extend the test generation strategy via plugin loading (e.g. the options arg to the Component object returned from function above is not needed for test coverage).
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Pynguin β Allow developers to generate Python unit tests automatically
Just in case you are looking for an alternative approach: if you write contracts in your code, you might also consider crosshair [1] or icontract-hypothesis [2]. If your function/method does not need any pre-conditions then the the type annotations can be directly used.
(I'm one of the authors of icontract-hypothesis.)
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Programming in Z3 by learning to think like a compiler
There's a tool for verification of Python programs based on contracts which uses Z3: https://github.com/pschanely/CrossHair
You can use it as part of your CI or during the development (there's even a neat "watch" mode, akin to auto-correct).
- Diff the behavior of two Python functions
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Finding Software Bugs Using Symbolic Execution
Looking at some of your SMT-based projects, I'd love to compare your SMT solver notes with my mine from working on https://github.com/pschanely/CrossHair
Sadly, there aren't a lot of resources on how to use SMT solvers well.
alive2
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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].
- 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
What are some alternatives?
pynguin - The PYthoN General UnIt Test geNerator is a test-generation tool for Python
klee - KLEE Symbolic Execution Engine
icontract-hypothesis - Combine contracts and automatic testing.
recreational-rosette - Some fun examples of solving problems with symbolic execution
angr - A powerful and user-friendly binary analysis platform!
zz - πΊπ ZetZ a zymbolic verifier and tranzpiler to bare metal C
Symbolica - Symbolica's open-source symbolic execution engine. [Moved to: https://github.com/Symbolica/Symbolica]
miasm - Reverse engineering framework in Python
llvm-tutor - A collection of out-of-tree LLVM passes for teaching and learning
boofuzz - A fork and successor of the Sulley Fuzzing Framework
Cassius - A CSS specification and reasoning engine