Verus seems to be exactly what I have been looking for! I discovered AllConcur[0] on HN a while back, and I wanted to port it to Go, but since it used TLA+ and C, and it was very confusing for me to understand how you can trust the implementation unless you can compile TLA+ to C.
I was talking to Gemini about comparing Verus to TLA+ and it said that TLA+ is usually used (for example) "to prove that a distributed consensus protocol is logically sound" but when I asked if Verus can do that too, it said yes. So Verus can be compiled and integrated with Rust, whereas TLA+ is used more for blueprint development that then guides the implementation in the mind of the implementer.
That is my pet peeve against TLA+ advocacy, the disassociation between a theoretical proof of a specific algorithm, data structures, and the actual implementation in production.
I rather push for tooling that allows code generation based on the formal proofs like FStart or Dafny, or is integrated with specific programming languages like SPARK, Frama-C or this Verus.
You can write everything in Lean and generate an implementation. Given that LLMs can now generate Lean proofs, this does not seem to be prohibitively expensive anymore. The real issue with distributed algorithms is that they are hard to reason about, and reasoning about them at the code level does not make the verification problem easier, it makes it harder.
Ive been using Rust with LLMs for the past 2 years already. It's just amazing, I can't really think anything else to code with. Normally, been testing my codes unit tests + integration tests where necessary. But, I will take a look at this seriously.
That's fascinating. Does it mean it verifies mathematical proofs directly within the Rust code? Does anyone know the underlying principles of how this is possible?
https://verus-lang.github.io/verus/publications-and-projects... - The papers here go into their implementation. They take the proof statements and information about the program and turn it into an SMT problem (and run it through Z3 if I read correctly) and then they use that to prove the properties of the program.
SPARK/Ada and Dafny work similarly, and have good documentation if you want to try your hand at something with a (presently) better set of documentation.
All it does is dispatch proof obligations to an SMT solver like Z3. There is nothing special about Verus, it works in the same way other program verification frameworks like Dafny and Frama-C work - except it's for Rust.
Most of this article presents nothing unique to Verus and is more of an advertisement for the authors research work and the other work AWS is doing.
What if the 'mathematical specification of its functionality' is incorrect? How to prove the correctness of the mathematical specification faster than the underlying environment, code and dependencies change?
IMO, formal verification is never going to work. It's very clear that a lot of people are desperate to see it used in mainstream software development, but every innovation which proponents have seen as an opportunity to finally prove its utility has only served to further discredit it.
Now proponents are at a point that they literally have to convince us that people who aren't able to write correct code are somehow able to write correct mathematical specifications!
This is quite an extraordinary claim given that the mathematical specification is an order of magnitude longer and more complex than the code itself... And every experienced software engineer knows that mistakes grow proportionally to the size of the logic... Unfortunately, mathematical spec is logic; just like code, except it's more complex and thus more error-prone.
And don't even get me started on the fact that APIs, engines and languages change constantly from under you and thus the mathematical spec would get completely invalidated every week or so each time you did an update. Unfortunately, even in the best case scenario, reality is always going to change and invalidate our proofs faster than we can publish them. By the time you've proven the theory, its underlying assumptions already ceased to hold true.
Even in a far simpler hypothetical world with just one piece of software; the software's own execution could potentially change the reality which it relied on to prove its own correctness and would thus invalidate its own correctness merely by executing.
> people who aren't able to write correct code are somehow able to write correct mathematical specifications
This describes about one or two thirds of the Theoretical CS academic community (conservative estimate) /s
> This is quite an extraordinary claim given that the mathematical specification is an order of magnitude longer and more complex than the code itself
I find this hard to believe. The mathematical specification for "array a is sorted" is "forall n in Nat: 0 < n < len(a) -> a[n] >= a[n+1]". The average sorting algorithm is usually a tad longer than this.
> the mathematical spec would get completely invalidated every week or so each time you did an update
Well of course nobody serious advocates for formalizing/verifying code that is subject to that much churn (be it internal or external).
> How to prove the correctness of the mathematical specification?
You can show that your specifications satisfy well-accepted criteria like confidentiality and integrity. This is usually done as the final verification step. For example, AWS just did it for the Nitro hypervisor used by EC2: https://aws.amazon.com/blogs/compute/aws-nitro-isolation-eng....
That means when one goes out of whack, someone will notice. E.g. let's say the mathematical property is correct, but someone optimizes some behavior. Without tests or verification that could break production or worse: not break it, but break security without anybody noticing.
I feel like the exact same line could be used to argue the opposite point against formal verification.
I'm not saying that proof is inherently bad. If it was free, then I agree it would be good, but my point is that it's not free. Proofs are expensive to produce, maintain, they lock-down flawed implementations, focus on correctness but disregard more important aspects like modularity (I.e. loose coupling, high cohesion). Also; formal proofs discourage change and they create false confidence about reliability because sometimes the bug is in the spec itself, especially as the spec gets more complicated.
I think modularity is a more useful property to aim for in terms of achieving the right degree of correctness over the life of the software, in a practical sense.
Formal proofs can work against modularity if the proof must be rewritten in order to achieve modularity as requirements change over time; which is the reality for most software.
Verus seems to be exactly what I have been looking for! I discovered AllConcur[0] on HN a while back, and I wanted to port it to Go, but since it used TLA+ and C, and it was very confusing for me to understand how you can trust the implementation unless you can compile TLA+ to C.
I was talking to Gemini about comparing Verus to TLA+ and it said that TLA+ is usually used (for example) "to prove that a distributed consensus protocol is logically sound" but when I asked if Verus can do that too, it said yes. So Verus can be compiled and integrated with Rust, whereas TLA+ is used more for blueprint development that then guides the implementation in the mind of the implementer.
Seems awesome!
[0]: https://news.ycombinator.com/item?id=12357976
That is my pet peeve against TLA+ advocacy, the disassociation between a theoretical proof of a specific algorithm, data structures, and the actual implementation in production.
I rather push for tooling that allows code generation based on the formal proofs like FStart or Dafny, or is integrated with specific programming languages like SPARK, Frama-C or this Verus.
You can write everything in Lean and generate an implementation. Given that LLMs can now generate Lean proofs, this does not seem to be prohibitively expensive anymore. The real issue with distributed algorithms is that they are hard to reason about, and reasoning about them at the code level does not make the verification problem easier, it makes it harder.
Ive been using Rust with LLMs for the past 2 years already. It's just amazing, I can't really think anything else to code with. Normally, been testing my codes unit tests + integration tests where necessary. But, I will take a look at this seriously.
see also https://lean-lang.org/use-cases/aeneas/ https://github.com/AeneasVerif/aeneas (by Microsoft)
>>The result is fast code that's more correct and secure than average.
Welcome to Rust
This seems like a big deal
That's fascinating. Does it mean it verifies mathematical proofs directly within the Rust code? Does anyone know the underlying principles of how this is possible?
https://verus-lang.github.io/verus/publications-and-projects... - The papers here go into their implementation. They take the proof statements and information about the program and turn it into an SMT problem (and run it through Z3 if I read correctly) and then they use that to prove the properties of the program.
SPARK/Ada and Dafny work similarly, and have good documentation if you want to try your hand at something with a (presently) better set of documentation.
https://mitpress.mit.edu/9780262546232/program-proofs/ - Dafny book, pretty good tutorial on the topic
https://learn.adacore.com/courses/intro-to-spark/chapters/01... - Free tutorial for SPARK
thanks!
All it does is dispatch proof obligations to an SMT solver like Z3. There is nothing special about Verus, it works in the same way other program verification frameworks like Dafny and Frama-C work - except it's for Rust. Most of this article presents nothing unique to Verus and is more of an advertisement for the authors research work and the other work AWS is doing.
What if the 'mathematical specification of its functionality' is incorrect? How to prove the correctness of the mathematical specification faster than the underlying environment, code and dependencies change?
IMO, formal verification is never going to work. It's very clear that a lot of people are desperate to see it used in mainstream software development, but every innovation which proponents have seen as an opportunity to finally prove its utility has only served to further discredit it.
Now proponents are at a point that they literally have to convince us that people who aren't able to write correct code are somehow able to write correct mathematical specifications!
This is quite an extraordinary claim given that the mathematical specification is an order of magnitude longer and more complex than the code itself... And every experienced software engineer knows that mistakes grow proportionally to the size of the logic... Unfortunately, mathematical spec is logic; just like code, except it's more complex and thus more error-prone.
And don't even get me started on the fact that APIs, engines and languages change constantly from under you and thus the mathematical spec would get completely invalidated every week or so each time you did an update. Unfortunately, even in the best case scenario, reality is always going to change and invalidate our proofs faster than we can publish them. By the time you've proven the theory, its underlying assumptions already ceased to hold true.
Even in a far simpler hypothetical world with just one piece of software; the software's own execution could potentially change the reality which it relied on to prove its own correctness and would thus invalidate its own correctness merely by executing.
> people who aren't able to write correct code are somehow able to write correct mathematical specifications
This describes about one or two thirds of the Theoretical CS academic community (conservative estimate) /s
> This is quite an extraordinary claim given that the mathematical specification is an order of magnitude longer and more complex than the code itself
I find this hard to believe. The mathematical specification for "array a is sorted" is "forall n in Nat: 0 < n < len(a) -> a[n] >= a[n+1]". The average sorting algorithm is usually a tad longer than this.
> the mathematical spec would get completely invalidated every week or so each time you did an update
Well of course nobody serious advocates for formalizing/verifying code that is subject to that much churn (be it internal or external).
> How to prove the correctness of the mathematical specification?
You can show that your specifications satisfy well-accepted criteria like confidentiality and integrity. This is usually done as the final verification step. For example, AWS just did it for the Nitro hypervisor used by EC2: https://aws.amazon.com/blogs/compute/aws-nitro-isolation-eng....
That means when one goes out of whack, someone will notice. E.g. let's say the mathematical property is correct, but someone optimizes some behavior. Without tests or verification that could break production or worse: not break it, but break security without anybody noticing.
That is worth the hassle for some applications.
So it moves from both "my implementation and specification is incorrect", to just "my specification is incorrect".
I don't understand this type of thinking. Proving what you can is still better. Don't let perfect be the enemy of good.
>> Don't let perfect be the enemy of good.
I feel like the exact same line could be used to argue the opposite point against formal verification.
I'm not saying that proof is inherently bad. If it was free, then I agree it would be good, but my point is that it's not free. Proofs are expensive to produce, maintain, they lock-down flawed implementations, focus on correctness but disregard more important aspects like modularity (I.e. loose coupling, high cohesion). Also; formal proofs discourage change and they create false confidence about reliability because sometimes the bug is in the spec itself, especially as the spec gets more complicated.
I think modularity is a more useful property to aim for in terms of achieving the right degree of correctness over the life of the software, in a practical sense.
Formal proofs can work against modularity if the proof must be rewritten in order to achieve modularity as requirements change over time; which is the reality for most software.