The internet discovers TLA+. Now what?
reasonable.io
reasonable.io
As a side note, there's a lot of talk about programming being not fulfilling anymore. But the above exercise was probably the most fun I've had with engineering in a long time, and would have been nearly impossible for me personally without AI. Perhaps it was the novelty of the TLA+ stuff, but I think it offers a glimpse into what our jobs could actually be in the future, beyond simply telling Claude to do what you used to do manually and then clicking enter. There are much more ambitious and fulfilling use cases for it.
I completely agree with you about how fun these things can be. I've long believed in automated testing methods that were beyond what I could justify. I learned about formal methods in college in the 1980s and have never been able to justify (even to myself) applying them. Now a volunteer project I've been working on has 100% standard test coverage, many property-based tests, and 1000s of formal verification tests. That combination has surfaced multiple bugs in widely used underlying libraries and a bug in Rosetta 2's Intel emulation that was affecting me. And that's all just on the testing front. I'm having a lot of fun with all this.
The result, while takes much longer, is quite magical. A lot of transaction and atomic bugs were found and fixed just by having such simple instruction alone.
However, sometimes it is not all magical especially around external resources. Cloudflare, unfortunately, sometimes have hiccups on D1 and KV with timeout, which is more or less a force majeure.
Fortunately, that means I will have to model the action as a binary event, that the transaction may not complete as we would have thought guaranteed, and by add extra guard around it, so that the state would have to be retried.
I was able to workaround it like that so far. Keep in mind the more conditions and constraints, the beefier your CPU might need since it is on the scale of NP
The Intel paper shows how TLA+ was applied as a step prior to writing the hardware description. I'm not sure if it caught on, it seems like other tools are used nowdays, does anyone here in the VLSI industry know?
Proving programs correct end-to-end (i.e. code to high-level properties) - as this company and others purport to do - is so difficult that humans have only been able to do it for very small programs (~10KLOC) and even then, in very specialised cases, where the programs have been written in an extra-simple way (often at the cost of performance, because performance often requires more complicated algorithms). If AI becomes at least an order of magnitude more capable than humans at software development, which is what will be required for this task, would it need our help to write various tools and harnesses that help with the task? After all, writing these tools is so much easier than using them for that goal that I don't understand the hypothesis behind AI capability here.
This company says: they're "developing the agentic frameworks to make these correctness guarantees accessible to all software engineers". But developing all that is the easy part! If AI can do the hard part, why does it need our help to make this accessible, it can surely find a way to do that easy part itself! It's like saying, "Soon we'll have a machine that can harness so much energy to boil an ocean; we've built a service that lets you order a taxi to take the machine to the beach!" Why would an AI that is so much better than us at writing software need our help writing any kind of software for it?
This is not true. It has been done. I’ve seen it done for an entire OS too. Humans are very capable of doing this. The issue is this is seldom done practically speaking because the effort is not worth the benefit when the program becomes too complex.
For simple programs and small domains it’s worth it. For example type checking. Type checking proves one aspect of your program (the types) is fully correct.
That might be what pron's talking about. seL4 is only 10-20K lines of code as far as I remember. Maybe you have another OS in mind, though.
Question to you: i can understand how proof verification like z3 or lean requires a special language and an inference engine; given that model checkers like tla+ are mostly about exploring possible program states and checking properties of such states and chains of states, i do not quite understand why it can't be done with a conventional imperative language to express state transitions and invariants - especially an interpreted one like python (esp with continuation support) or a language targeting a vm like wasm where one should be able to snapshot program state?
As to why TLA+ is better at describing systems than programming languages, the reason is that it's much more general. It can say things like "a routine that sorts in a quadratic number of steps or less" rather than a specific sorting algorithm, and it allows stating (and proving) that a specific sorting algorithm matches that description or not. Most TLA+ formulas are too abstract to be run by a computer (i.e. they describe too many potential algorithms), but that's exactly what makes them useful to describe things when either you don't care about the details or you want to show that a particular algorithm implements a general property.
BTW, even algorithms like Quicksort are, themselves, too general to be accurately described by a programming language (i.e. a language that can be executed). Quicksort doesn't specify how a pivot is chosen (it doesn't matter for the correctness), it doesn't specify how that partitioning is done (ditto), and it doesn't specify in what order the recursion is done or perhaps even in parallel (ditto). Yet a computer needs to be told all these details to run an implementation of Quicksort, even though the algorithm works, and can be proven to work, no matter what these details are. In a language like TLA+ you can say how to choose a pivot or you can say "a pivot is somehow chosen" (which covers all possible mechanisms for choosing one).
Also, TLA+ is much simpler than a programming language and obeys simple and intuitive substitution rules - e.g. `x = 3` is equivalent to `3 = x` and `x = y + 1` is (almost) equivalent to `x - y = 1`, which is what you want when you're after clarity. It's just different from programming languages (because it's maths), so it's a different, though simpler, kind of language to learn.
Doesn't it only need to become an order of magnitude more capable than humans at theorem proving, not general software development?
The AI tools are great at lowering the learning curve by changing "how would I possibly express this" to "ah, let's see if this expression of it is actually right?" and "hm, is there a simpler way to express the same thing?"
Like StackOverflow for javascript questions, but for an area that was far to obscure to have a good library of example answers.
I'm not looking to prove the entirety of every system. Usually just some core bits. And often not connected automatically to the code (which may not be gonna change much).
Now that we have assistants that excel at tedious and time consuming tasks, it’ll be interesting to see if the dominant approach is still using dedicated modeling languages like TLA+ with generated verification harnesses. Or if it’s easier to use something closer to the implementation, similar to what the Stateright project [1] is trying to do.
* It will brute force all states.
* You can add a variety of assertions.
TLA+ is a specific kind of formal verification framework for software. The overall idea is that you can "prove" that some software works, rather than the typical "seems like it works" we aim for.
But under the covers, all formal verification schemes are (imho) best viewed as "very fancy testing". There are a few kinds of this fancy testing. TLA+ is the kind that can auto-generate all the relevant test cases for your code (there's more to it than that, but this definition works for now). So now instead of "I wrote a bunch of test cases, all that I could think of, and they pass" you have "I used TLA+ so I know I'm exercising all the possible test cases and they pass".
Here's the problem though: to achieve that trick, you have to write your code in a special language (TLA+). It isn't a tool that can be just pointed at regular production code.
So what you get to "prove" is a translation of your actual code into TLA+ code. It may be possible to auto-translate one to the other (I asked the original author if they did that, but no reply yet). Usually it's a manual process. Therefore you have "proof" but not quite as you know it, because you proved something different than what runs. But still more useful than a wet finger raised into the wind.
For this reason it's typically only used on narrow risky pieces of code (quorum voting is the canonical use case).
I am curious about the limits of what one can expect this language to solve and therefore the types of programs/domains to which it is more/less suited. Why is determining quorum the canonical use case?
This is misleading. Testing and formal verification are vastly different.
https://news.ycombinator.com/item?id=48287718
With link to pdf, github
TLA gives you a full toolbox and in theory can model whatever you can express. That's very different from a design pattern.
No wonder GitHub keeps going down.
https://tlaplus-process-studio.com/
https://github.com/RCSnyder/tlaplus-process-studio
Also the hard part is connecting the tla+ spec to the implementation code with traces
I guess if they found real verifiable bugs then it's good
Don't waste your time.