MIRAI VS loom

MIRAI

Do you use the Cargo "contracts" for Design-by-Contract style invariants that plugs into Facebook's MIRAI prover thing?

I always thought it this was super neat:

https://crates.io/crates/contracts

https://github.com/facebookexperimental/MIRAI/blob/main/exam...

  [dependencies]

Here's a 2020 overview of Rust verification tools https://alastairreid.github.io/rust-verification-tools/ - it says

> Auto-active verification tools

> While automatic tools focus on things not going wrong, auto-active verification tools help you verify some key properties of your code: data structure invariants, the results of functions, etc. The price that you pay for this extra power is that you may have to assist the tool by adding function contracts (pre/post-conditions for functions), loop invariants, type invariants, etc. to your code.

> The only auto-active verification tool that I am aware of is Prusti. Prusti is a really interesting tool because it exploits Rust’s unusual type system to help it verify code. Also Prusti has the slickest user interface: a VSCode extension that checks your code as you type it!

> https://marketplace.visualstudio.com/items?itemName=viper-ad...

Now, on that list, there is also https://github.com/facebookexperimental/MIRAI that, alongside the crate https://crates.io/crates/contracts (with the mirai_assertion feature enabled) enables writing code like this

    #[ensures(person_name.is_some() -> ret.contains(person_name.unwrap()))]

Traditional design by contract checks the contracts at runtime. They can be understood as a form of dynamic typing with quite complicated types, which may be equivalent to refinement types

But you can check contracts at compile time too. It's quite the same thing as static typing with something like refinement types. That's because, while with contracts we can add preconditions like "the size of this array passed as parameter must be a prime number", with refinement types we can define the type of arrays whose size is a prime number, and then have this type as the function argument. (likewise, postconditions can be modeled by the return type of the function)

See for example this Rust library: https://docs.rs/contracts/latest/contracts/

It will by default check the contracts at runtime, but has an option to check them at compile time with https://github.com/facebookexperimental/MIRAI

Now, this Rust library isn't generally understood as creating another type system on top of Rust, but we could do the legwork to develop a type theory that models how it works, and show the equivalence.

Or, another example, Liquid Haskell: https://ucsd-progsys.github.io/liquidhaskell/ it implements a variant of refinement types called liquid types, which is essentially design by contract checked at compile type. In this case, the type theory is already developed. I expect Liquid Haskell to be roughly comparable to Rust's contracts checked by MIRAI.

Now, what we could perhaps say is that refinement types are so powerful that they don't feel like regular types! And, while that's true, there are type systems even more powerful: dependent types used in languages like Coq, Lean and F* to prove mathematical theorems (your type is a theorem, and your code, if it typechecks, is a proof of that theorem).

Dependent types were leveraged to create a verified TLS implementation that mathematically proves the absence of large class of bugs, miTLS https://www.mitls.org/ (they discovered a number of vulnerabilities in TLS implementations and proved that their implementation isn't vulnerable), and HACL* https://github.com/hacl-star/hacl-star a verified crypto implementation used by Firefox and Wireguard. They are part of Project Everest https://project-everest.github.io/ which aims to develop provably secure communications software.

Because it's convenient and familiar to most programmers. Not providing bounds-checked indexing makes some kinds of code very hard to write.

But note his problem also happens with integer division.

In Rust, a[x] on an array or vec is really a roughly a shortand for a.get(x).unwrap() (with a different error message)

Likewise, a / b on integers is a kind of a shortand for a.checked_div(b).unwrap()

The thing is, if the index ever is out of bounds, or if the denominator is zero, the program has a bug, 100% of time. And if you catch a bug using an assertion there is seldom anything better than interrupting the execution (the only thing I can think of is restarting the program or the subsystem). If you continue execution past a programming error, you may sometimes corrupt data structures or introduce bizarre, hard to debug situations.

Doing a pattern match on a.get(x) doesn't help because if it's ever None (and your program logic expects that x is in bounds) then you are kind of forced to bail.

The downside here is that we aren't catching this bug at compile time. And it's true that sometimes we can rewrite the program to not have an indexing operation, usually using iterators (eliding the bounds check will make the program run faster, too). But in general this is not possible, at least not without bringing formal methods. But that's what tests are for, to ensure the correctness of stuff type errors can't catch.

Now, there are some crates like https://github.com/dtolnay/no-panic or https://github.com/facebookexperimental/MIRAI that will check that your code is panic free. The first one is based on the fact that llvm optimizations can often remove dead code and thus remove the panic from a[x] or a / b - if it doesn't, then compilation fails. The second one employs formal methods to mathematically prove that there is no panic. I guess those techniques will eventually be ported to the kernel even if panics happen differently there (by hooking on the BUG mechanism or whatever)

There's a MIR Abstract interpreter project: https://github.com/facebookexperimental/MIRAI

Nice, I just would have liked to get all these different verification tools combined under the same interface, just being different backends as drafted by the rust verification tools work of project oak: have "cargo verify" as common command and use common test annotations, allowing the same test to be verified with different backends or just fuzzed/proptested.

The model checking approach seems to be a bit limited regarding loops. There are also abstract interpreters, such as https://github.com/facebookexperimental/MIRAI, and symbolic executers, such as https://github.com/dwrensha/seer or https://github.com/GaloisInc/crucible.

Overall I believe this space would benefit from more coordination and focus on developing something that has the theoretical foundations to cover as many needs as possible and then make a user-friendly tool out of it that is endorsed by the Rust project similar to how Rust analyzer is the one language server to come.

https://github.com/facebookexperimental/MIRAI which integrates with https://crates.io/crates/contracts (a crate that does runtime checking of contracts, and with mirai they are upgraded to compile-time checking) and https://crates.io/crates/mirai-annotations

With the mirai_assertions feature, it can use the MIRAI static analyzer (though it requires nightly).

> Mutexes are very cheap in the uncontended case

It was a while ago I was deep into this mess so forgive any ignorance–but–iirc the thread-mutex dogma[1] has many pitfalls despite being so widely used. Primarily they’re easy to misuse (deadlocks, holding a lock across a suspend point), and have unpredictable performance because they span so far into compiler, OS and CPU territory (instruction reordering, cache line invalidation, mode switches etc). Also on Arm it’s unclear if mutices are as cheap because of the relaxed memory order(?). Finally code with mutices are hard to test exhaustively, and are prone to heisenbugs.

Now, many if not most of the above apply to anything with atomics, so lock-free/wait-free won’t help either. There’s a reason why a lot of concurrency is ~phd level on the theoretical side, as well as deeply coupled with the gritty realities of hardware/compilers/os on the engineering side.

That said, I still think there’s room for a slightly expanded concurrency toolbox for mortals. For instance, a well implemented concurrent queue can be a significant improvement for many workflows, perhaps even with native OS support (io_uring style)?. Another exciting example is concurrency permutation test frameworks[2] for atomics that reorder operations in order to synthetically trigger rare logical race conditions. I’ve also personally had great experience with the Golang race detector. I hope we see some convergence on some of this stuff within a few years. Concurrency is still incredibly hard to get right.

[1]: I say this only because CS degrees has preached mutices to as the silver bullet for decades.

[2]: https://github.com/tokio-rs/loom

Of course atomics are absolutely essential for some of the libraries we take for granted, such as Arc and Tokio. But if you start reading the code and comments and issues and PRs around code like that, you'll see how much work it took to mature them to the point we can now rely on them. That's why tools like Loom exist.

loom and shuttle can help you narrow down the problem.

Unless you are writing unsafe code, you generally don't need to use sanitizers. If you do write unsafe code, checking it with a sanitizer would be a great idea. Two most useful tools here I think are miri and loom.

https://github.com/tokio-rs/loom perhaps? It also models weak memory reordering, but takes some work to integrate into existing apps.

For triggering race conditions in compiled binaries, you could try https://robert.ocallahan.org/2016/02/introducing-rr-chaos-mo....

There is Loom[1] (part of the Tokio project) for exhaustively testing multithreaded code. Though as far as I can tell it is designed for debugging threads, not async tasks.

[1] https://github.com/tokio-rs/loom

That tool seems similar to https://github.com/tokio-rs/loom, insofar as detecting potential locking errors. These are useful during development, but could still miss production cases (as dev never perfectly matches production). This crate is meant to not have to worry about possibly deadlocking.

Rust doesn't catch memory ordering errors, which can result in behavioral bugs in safe Rust and data races and memory unsafety in unsafe Rust. But Loom is an excellent tool for catching ordering errors, though its UnsafeCell API differs from std's (and worse yet, some people report Loom returns false positives/negatives in some cases: https://github.com/tokio-rs/loom/issues/180, possibly https://github.com/tokio-rs/loom/issues/166).