The formal verification landscape for programming languages continues to evolve, with tools like coq-of-rust aiming to mathematically prove code correctness in Rust applications. However, community discussions reveal important nuances about what formal verification can and cannot guarantee, alongside news of a significant name change in the verification ecosystem.
The Reality of Formal Verification Claims
While coq-of-rust promises to make applications with no bugs through mathematical proofs, the developer community has highlighted an important distinction. Formal verification proves that code correctly implements a specification, but cannot guarantee that the specification itself is free of flaws.
I like formal verification. But I consider this a misrepresentation of what it offers. It lets you mathematically prove that your code implements a specification correctly. It doesn't prove that your specification is bug-free & vulnerability-free. It doesn't prove that your specification correctly implements your business rules.
This distinction between verification (does the code match the spec?) and validation (does the spec match the actual needs?) represents a fundamental concept in quality assurance. Formal verification excels at proving invariants hold for all possible inputs, which is particularly valuable where specifications are simpler than implementations, such as in databases and file systems.
Coq Becomes Rocq: A Significant Renaming
A noteworthy development mentioned in the comments is that the Coq proof assistant, the foundation of coq-of-rust, has been renamed to Rocq. According to community discussions, this change resulted from a community survey where approximately 24% of users expressed discomfort with the original name, finding it uncomfortable or awkward to use in professional settings. The project is now officially known as Rocq and can be found at rocq-prover.org.
The renaming has sparked mixed reactions, with some viewing it as a necessary professional evolution while others lament the loss of what they considered harmless wordplay.
Rust's Unique Position in Verification
Rust's mutable-xor-aliased memory model makes it particularly suitable for formal verification compared to many other languages. This model, where data can either be mutable or have multiple references but not both simultaneously, creates a foundation that verification tools can build upon more effectively.
Community members noted that without such a model, verification becomes just as hard — and just as practically intractable — as all the verifiers for existing languages. This gives Rust-based verification tools like coq-of-rust, Verus, and others a potential advantage in the verification space.
Formal Verification Tools for Rust Mentioned in Comments
- coq-of-rust: Translates Rust to Coq for formal verification
- Verus: Aimed at full systems verification (verified filesystems, Kubernetes controllers)
- Creusot: Translation from MIR to Why3 (and then SMT solvers)
- Ironclad/Gloire: Not Rust-specific, but a formally verified OS kernel written in SPARK/Ada
Key Points About Formal Verification
- Proves code implements a specification correctly
- Does not prove the specification itself is correct
- Particularly useful when specification is simpler than implementation
- Benefits from Rust's "mutable-xor-aliased" memory model
Alternative Approaches and Competitors
The formal verification landscape extends beyond coq-of-rust. Community members highlighted several alternatives, including Verus for systems verification (used in verified filesystems and Kubernetes controllers), and the Ironclad kernel with Gloire OS built on top of it using SPARK and Ada.
Some developers expressed interest in seeing similar verification approaches applied to other languages like Zig, particularly through tools like Zig AIR, suggesting an appetite for formal verification across the systems programming ecosystem.
The pursuit of mathematically proven code correctness continues to attract interest across programming communities, though with a more nuanced understanding of what formal verification can realistically deliver. As tools mature and approaches diversify, the gap between theoretical perfection and practical implementation gradually narrows, even if the elusive goal of 100% bug-free code remains more aspiration than reality.