Key detail in the actual memo is that they’re not using just an LLM. “Wallach anticipates proposals that include novel combinations of software analysis, such as static and dynamic analysis, and large language models.”

They also are clearly aware of scope limitations. They explicitly call out some software, like entire kernels or pointer arithmetic heavy code, as being out of scope. They also seem to not anticipate 100% automation.

So with context, they seem open to any solutions to “how can we convert legacy C to Rust.” Obviously LLMs and machine learning are attractive avenues of investigation, current models are demonstrably able to write some valid Rust and transliterate some code. I use them, they work more often than not for simpler tasks.

TL;DR: they want to accelerate converting C to Rust. LLMs and machine learning are some techniques they’re investigating as components.

This is an interesting application of so-called AI, where the result is actually desirable and isn’t some sort of frivolity or grift. The memory-safety guarantees offered by native Rust code would be a very welcome improvement over C code that guarantees very little. So a translation of legacy code into Rust would either attain memory safety, or wouldn’t compile. If AI somehow (very unlikely) manages to produce valid Rust that ends up being memory-unsafe, then it’s still an advancement as the compiler folks would have a new scenario to solve for.

Lots of current uses of AI have focused on what the output could enable, but here, I think it’s worth appreciating that in this application, we don’t need the AI to always complete every translation. After all, some C code will be so hardware-specific that it becomes unwieldy to rewrite in Rust, without also doing a larger refactor. DARPA readily admits that their goal is simply to improve the translation accuracy, rather than achieve perfection. Ideally, this means the result of their research is an AI which knows its own limits and just declines to proceed.

Assuming that the resulting Rust is: 1) native code, and 2) idiomatic, so humans can still understand and maintain it, this is a project worth pursuing. Meanwhile, I have no doubt grifters will also try to hitch their trailer on DARPA’s wagon, with insane suggestions that proprietary AI can somehow replace whole teams of Rust engineers, or some such nonsense.

Edit: is my disdain for current commercial applications of AI too obvious? Is my desire for less commercialization and more research-based LLM development too subtle? :)

“You can go to any of the LLM websites, start chatting with one of the AI chatbots, and all you need to say is ‘here’s some C code, please translate it to safe idiomatic Rust code,’ cut, paste, and something comes out, and it’s often very good, but not always,” said Dan Wallach, DARPA program manager for TRACTOR, in a statement.

“This parlor trick impressed me. I’m sure it can scale to solve difficult real world problems.”

It’s a promising approach worth trying, but I won’t be holding my breath.

If DARPA really wanted safer languages, they could be pushing test coverage, not blindly converting stable well tested C code into untested Rust code.

This, like most AI speculation, reeks of looking for shortcuts instead of doing the boring job at hand.