That's what LLMs are for - idiom translation. You can't trust them to do it right, though.

[Pan et al . 2024] find that while GPT-4 generates code that is more idiomatic than C2Rust, only 61% of it is correct (i.e., compiles and produces the expected result), compared to 95% for C2Rust.

This problem needs both AI-type methods to help with the idioms and formal methods to insure that the guessed idioms correctly capture the semantics.

A big advance in this project is that they can usually translate C pointer arithmetic into Rust slices. That's progress on of one of the hardest parts of the problem. C2Rust did not do that. That system just generates unsafe raw pointer arithmetic, yielding ugly Rust code that replicates C pointer semantics using function calls.

DARPA is funding research in this area under the TRACTOR program. Program awards in April 2025, so this is just getting started. It's encouraging to see so much progress already. This looks do-able.

Optimizing C compilers also happened to be good at idiom recognition, and we can probably trust them a little more. The OP paper does mention future plan to use clang as well: >We have plans for a libclang-based frontend that consume actual C syntax.

If such transformation can be done at IR level it might be more efficient to be to C-IR > idiom transform to Rust-IR > run safe-checks in Rust-IR > continue compilation in C-IR or Rust-IR or combining both for better optimization properties.

If your experience with something less than half as good as state-of-the-art is that it worked 66% of the time, I am not sure why you would be so dismissive about the future potential.

https://dl.acm.org/doi/pdf/10.1145/3597503.3639226

> As for C2Rust, the 5% unsuccessful translations were due to compilation errors, the majority of them caused by unused imports.

I'm rather confused what that means, since unused imports cause warnings, not errors in Rust.