Hopefully the proof would break if one tried to transfer it over?
If you have INT_MIN along with any other negative number in the array then your program has undefined behavior in C. Signed integer overflow is UB (but unsigned overflow is not).
What? Why? There’s no addition needed to solve this problem. The example implementation does invert each element, which is undefined for INT_MIN, but it would be trivial to just skip INT_MIN elements (since their additive inverse is never in the set).
`INT_MIN + -1` is not 0 so it should report false in that case.
For UINT_MAX, the algorithm would need to be reconsidered, though, since it's written with signed integers in mind.
> Hopefully the proof would break if one tried to transfer it over?
Hopefully. The proof would have to be modified to account for the actual types. If you're using bounded integers you'd need to write a different proof.
The algorithm is written assuming that unary - produces the additive inverse. That is also true for C's unsigned integers. -1U == UINT_MAX, -UINT_MAX == 1U. It Just Works.
The proof is correct in the language it's written for, Lean. If you change the context (axioms) of a proof then the proof may be invalidated. This is not a surprising thing to anyone who spends a second thinking about it.
If I can specify the type of my input I can ensure the verification.
For example, Rust's borrow checker guarantees* memory safety of any code written in Rust, even a 10M+ LOC project. Another example is sel4, a formally-verified micro-kernel (https://sel4.systems/About/seL4-whitepaper.pdf).
* Technically not; even if the code doesn't use `unsafe`, not only is Rust's borrow checker not formally verified, there are soundness holes (https://github.com/rust-lang/rust/issues?q=is%3Aopen%20is%3A...). However, in theory it's possible to formally prove that a subset of Rust can only encode memory-safe programs, and in practice Rust's borrow checker is so effective that a 10M+ LOC project without unsafe still probably won't have memory issues.
If I access beyond the end of an array in Rust, the panic handler runs and starts unwinding my stack. If I access beyond the end of an array in C++ with .at() the excwption handler runs and starts unwining my stack. If I access beyond the end of an array in C the SIGSEGV handler may (*) run and I could, if I wanted to, start unwinding my stack.
Ah, but in C, sometimes if I access the wrong memory, I get garbadge instead of a panic.
Sure, and if I store my data in a Rust array and store indexes into that array around the place as sort of weak references (something I've seen Rust programmers use and talk about all the time), I can easily fetch the wrong data too.
Rust provides a robust type system and a borrow checker which avoids a lot of common problems at the expence of adhering to a particular programming style. That's fine. That's worth advocating for.
But it's no pannacea. Not even close.
My favorite memory about this is a programmer lambasting Go's strings (which are basically immutable byte vectors) for not enforcing UTF-8, like Rust strings.
He then said that this means that in Go you can print filenames to the screen that can break your terminal session because of this if they contain invalid UTF-8, which Rust forces you to escape explicitly. The irony, of couse, is that the characters that can break your terminal session are perfectly valid UTF-8.
Rust's type safety convinced this guy that his Rust program was immune to a problem that it was simply not immune to.
Specifically, the relationship is between the _specification_ and the proof, and it was done for proofs written in Isabelle and not Lean.
The good news is that more and more automation is possible for proofs, so the effort to produce each proof line will likely go down over time. Still, the largest full program we've fully verified is much less than 100,000 LOC. seL4 (verified operating system) is around 10,000 lines IIRC.
Though it really does feel like we're still scratching the surface of proof writing practices. A lot of proofs I've seen seem to rely only on very low level building blocks, but stronger practitioners more immediately grab tools that make stuff simpler.
I would say, though, that it feels likely that your proofs are always going to be at least within an order of magnitude of your code, because in theory the longer your code is the more decision points there are to bring up in your proof as well. Though automatic proof searches might play out well for you on simpler proofs.
I don't think this is true at all. For many kinds of programs that it would be good to have formal verification for, all of the details are very important. For example, I'd love to know that the PET scan code was formally verified, and that it's impossible to, say, show a different radiation dose on the screen than the dose configured in the core. I very much doubt that it's easy to write a proof that the GUI controls are displaying the correct characters from a font, though.
Or, it would be good to know that the business flows in the company ERP are formally verified to actually implement the intended business processes, but even the formal specification for the business processes in a regular large company would likely take longer to produce than it takes for those same business processes or external laws to change.
Combining LLMs + formal methods/model checkers is a good idea, but it's far from simple because rolling the dice on some subsymbolic stochastic transpiler from your target programming language towards a modeling/proving language is pretty suspect. So suspect in fact that you'd probably want to prove some stuff about that process itself to have any confidence. And this is a whole emerging discipline actually.. see for example https://sail.doc.ic.ac.uk/software/
This article caught my eye because it's focused on imperative programming, and I've been very focused on declarative vs imperative programming over the last few years. I implemented a version of your function in CLIPS, a Rules-based language that takes a declarative approach to code:
(defrule sum-is-0 (list $? ?first $? ?second $?) (test (= 0 (+ ?first ?second))) => (println TRUE))
(defrule sum-is-not-0 (not (and (list $? ?first $? ?second $?) (test (= 0 (+ ?first ?second))))) => (println FALSE))
(assert (list 1 0 2 -1)) (run) (exit)
The theorem you write in Lean to prove the function kind-of exists in CLIPS Rules; you define the conditions that must occur in order to execute the Right Hand Side of the Rule. Note that the above simply prints `TRUE` or `FALSE`; it is possible to write imperative `deffunction`s that return values in CLIPS, but I wanted to see if I could draw parallels for myself between Lean code and theorems. Here's a gist with the simple version and a slightly more robust version that describes the index at which the matching numbers appear: https://gist.github.com/mrryanjohnston/680deaee87533dfedc74b...
Thank you for writing this and for your work on Lean! This is a concept that's been circling in my head for a minute now, and I feel like this article has unlocked some level of understanding I was missing before.
I have found that while there is a learning curve to programming using only recursion for looping, code quality does go significantly up under this restriction.
Here is why I personally think tail recursion is better than looping: with tail recursion, you are forced to explicitly reenter the loop. Right off the bat, this makes it difficult to inadvertently write an infinite loop. The early exit problem is also eliminated because you just return instead of making a recursive call. Moreover, using recursion generally forces you to name the function that loops which gives more documentation than a generic for construct. A halfway decent compiler can also easily detect tail recursion and rewrite it as a loop (and inline if the recursive function is only used in one place) so there need not to be any runtime performance cost of tail recursion instead of looping.
Unfortunately many languages do not support tail call optimization or nested function definitions and also have excessively wordy function definition syntax which makes loops more convenient to write in those languages. This conditions one to think in loops rather than tail recursion. Personally I think Lean would be better if it didn't give in and support imperative code and instead helped users learn how to think recursively instead.
I don't know why, but I have actually gotten a bit stronger on the imperative divide in recent years. To the point that I found writing, basically, a GOTO based implementation of an idea in lisp to be easier than trying to do it using either loops or recursion. Which, really surprised me.
I /think/ a lot of the difference comes down to how localized the thinking is. If I'm able to shrink the impact of what I want to do down to a few arguments, then recursion helps a ton. If I'm describing a constrained set of repetitive actions, loops. If I'm trying to hold things somewhat static as I perform different reduction and such, GOTO works.
I think "functional" gets a bit of a massive boost by advocates that a lot of functional is presented as declarative. But that doesn't have to be the case. Nor can that help you, if someone else hasn't done the actual implementation.
We can get a long way with very mechanical transformations, in the form of compilation. But the thinking can still have some very imperative aspects.
It’s a common enough problem that the “why is my program crashing” website is basically named after it.
Tail recursion in particualr is the least human-friendly way to represent looping. It mixes input and output parameters of the function in any but the most trivial cases. It also forces the caller to figure out what is the base value for all of the output parameters (often requiring a separate function just to provide those to callers). And it basically takes an implementation detail and makes it a part of your function interface.
Even in math, you typically define recursive functions like f(x) = 1 + f(x-1), not f(x, y) = f(x-1, y+1); g(x) = f(x, 0).
Also, when writing for loops, you have to explicitly think about your exit condition for the loop, and it is visible right there, at the top of the loop, making infinite loops almost impossible.
Maybe you don't care about performance; IMO squeezing performance is one of the important applications for formal verification, as you can prove your fast insane algorithm is correct, whereas the slow obvious one is obviously correct.
If your main concern is clarity, some things are clearer when written imperatively and some when written functionally.
Although it’s hard to fault the simple elegance of recursion!
* Scheme
* Haskell
* Elixir
* Erlang
* OCaml
* F#
* Scala
* (not Clojure)
* the JVM could remove tail-recursive calls, but IIRC this still hasn't been added for security reasons
* Racket
* Zig
* Lua
* Common Lisp, under certain compilers/interpreters
* Rust? (depends)
* Swift? (sometimes)
TCO is more general than that in some languages where any function call in tail position can be turned into a direct jump. This obviously requires either 1) runtime support in some form or 2) a non-trivial amount of program transformation during compilation.
[0] Scala's @tailrec is one I'm 100% certain of.
The .NET CLR supports the ‘.tail’ opcode which means that any .NET based language could support it. I’m hoping one day the C# team will get around to it. It seems like such low hanging fruit.
