[pron]

> but for 99% of mathematics there is a far more direct link to pure functions.

But state transitions are pure! Deterministic state machines are nothing more than mathematical functions from one algorithm state to the next, and nondeterministic ones are binary relations! (see this for an overview: http://research.microsoft.com/en-us/um/people/lamport/pubs/s...) Those can then be mathematically manipulated, substituted and what-have-you elegantly and simply. This is how formal verification has been reasoning about programs since the seventies (early on the formalization was a bit more cumbersome, but the concept was as simple).

> For example, look at the various proof assistants like Coq.

For example, look at the specification language (and proof assistant) TLA+. It is far simpler than Coq, just as pure, and requires no more than highschool math.

Coq and other PFP formalizations are built on the core idea of denotational semantics, an approximation which at times only complicates the very simple math of computation, requiring exotic math like category theory.

> the manipulation of formulas and proofs fits very well with an immutable, functional approach.

The manipulation of formulas and proofs fits very well with the imperative approach, only no category theory is required, and core concepts such as simulation/trace-inclusion arise naturally without need for complex embeddings. "PFP math" is what arises after you've decided to use denotational semantics. The most important thing is not to confuse language -- for which PFP has an elegant albeit arcane mathematical formalization -- with the very simple mathematical concepts underlying computation.

PFP is an attempt to apply one specific mathematical formulation to programming languages (other similar, though perhaps less successful attempts are the various process calculi). But it is by no means the natural mathematics of computation. Conflating language with algorithms is a form of deconstructionism: an interesting philosophical concept, but by no means a simple one, and perhaps not the most appropriate one for a science. Short of that, we have the basic, simple core math, and on top of it languages, some espousing various mathematical formalizations, related to the core math in interesting ways, and some less mathematical. But the math of computation isn't created by the language!

[johncolanduoni]

> But state transitions are pure! Deterministic state machines are nothing more than (obviously, "pure") functions from one program state to the next, and nondeterministic ones are binary relations!

Okay, yes, but 99% of mathematics isn't deterministic state machines. So again, pure functions can model a lot of things, but deterministic state machines are foreign to the way people think about mathematics in most instances.

> For example, look at the pure specification language (and proof assistant) TLA+. It is far simpler than Coq, just as pure, and requires no more than highschool math.

Okay, that's completely fine as an example. I just mentioned Coq because it's one of the better known ones.

> Coq and other PFP formalizations are built on the core idea of denotational semantics, an approximation which at times only complicates the very simple math of computation, requiring exotic math like category theory.

I'm not sure where CiC (or any of the other recent type theories, including HOTT) requires category theory (which is hardly exotic in 2016). People who are doing meta-mathematics on these systems are interested in categorical models and such, but implementing and using these systems requires no category theory.

Categories are not fundamental objects in these theories; you have to build them just like you do in material set theories, although some parts of that process are easier (especially in HOTT). But that is the reverse of the link that you're claiming.

> The manipulation of formulas and proofs fits very well with the imperative approach, only no category theory is required, and core concepts such as simulation/trace-inclusion arise naturally without need for complex embeddings.

Again, no category theory is required (though I'm still not sure why this is a bad thing?) to develop a prover like Coq or TLA+. If you're bringing simulation and trace-inclusion into this, then you're just saying the stateful, imperative approach is well adapted to working with stateful, imperative systems. I agree, but how exactly does that equate to that approach having any benefit whatsoever for formalizing the rest of mathematics?

[pron]

> Okay, yes, but 99% of mathematics isn't deterministic state machines. So again, pure functions can model a lot of things, but deterministic state machines are foreign to the way people think about mathematics in most instances.

Sorry, I wasn't clear. 99% of mathematics isn't Schrödinger's equation either, but Schrödinger's equation is still relatively simple math. State machines are simple math, but math isn't state machines. State machines are the concept that underlies computation, and simple math is used to reason about them.

> I just mentioned Coq because it's one of the better known ones.

... and one of the least used ones, at least where software is concerned. There's a reason for that: it's very hard (let alone for engineers) to reason about computer programs in Coq; it's much easier (and done by engineers) to reason about computer programs in TLA+ (or SPIN or B-Method or Alloy).

> which is hardly exotic in 2016

I think it's safe to say that most mathematicians in 2016 -- let alone software engineers -- are pretty unfamiliar with category theory, and have hardly heard of type theory. Engineers, however, already have nearly all the math they need to reason about programs and algorithm in the common mathematical way (they just may not know it, which is why I so obnoxiously bring it up whenever I can, to offset the notion that is way overrepresented here on HN that believes that "PFP is the way to mathematically reason about programs". It is just one way, not even the most common one, and certainly not the easiest one).

> but implementing and using these systems requires no category theory.

Right, but we're talking about foundational principles of computation. Those systems are predicated on denotational semantics, which is a formalization that identifies a computation with the function it computes (yes, some of those systems also have definitional equality, but still, denotational semantics is the core principle), rather than view the computation as built up from functions (in fact, this is precisely what monads do and why they're needed, as the basic denotational semantics fails to capture many important computations). This formalization isn't any better or worse (each could be defined in terms of the other), but it is more complicated, and is unnecessary to mathematically reason about programs. It does require CT concepts like monads to precisely denote certain computations.

> If you're bringing simulation and trace-inclusion into this, then you're just saying the stateful, imperative approach is well adapted to working with stateful, imperative systems.

There are no "imperative systems". Imperative/functional is a feature of the language used to describe a computation, not the computation itself (although, colloquially we say functional/imperative algorithms to refer to those algorithms that commonly arise when using the different linguistic approaches). The algorithm is always a state machine (assuming no textual deconstructionism) -- whether expressed in a language like Haskell or in a language like BASIC -- and that algorithm can be reasoned about with pretty basic math. And I am not talking about a "stateful" approach, but a basic mathematical approach based on state machines (a non-stateful pure functional program also ultimately defines a state machine).

> I agree, but how exactly does that equate to that approach having any benefit whatsoever for formalizing the rest of mathematics?

Oh, I wasn't talking about a new way to formalize the foundation of mathematics (which, I've been told, is the goal of type theory), nor do I think that a new foundation for math is required to mathematically reason about computation (just as it isn't necessary to reason about physics). I just pointed out that algorithms have a very elegant mathematical formulation in "simple" math, which is unrelated to PFP. This formulation serves as the basis for most formal reasoning of computer programs.

[johncolanduoni]

> I think it's safe to say that most mathematicians in 2016 -- let alone software engineers -- are pretty unfamiliar with category theory

I'm sure that's true for software engineers, but my experience is that category theory has permeated most fields to a significant degree. Many recent graduate-level texts on fields of mathematics from topology to differential geometry to algebra incorporate at least basic category theory like functors and natural transformations. It's even more common at the research level. And I say all of this not having actually met a single actual category theorist, only those in other fields who used at least some of it.

[tome]

> Those systems are predicated on denotational semantics, which is a formalization that identifies a computation with the function it computes ... rather than view the computation as built up from functions

You know there's operational semantics too, right? Operational semantics typically describes the behaviour of your program as a state machine, especially the small step type of operational semantics.

[pron]

Of course. But I was talking about FP there, and, I think, operational semantics must be embedded in FP. In the state machine view, operational semantics are simply a refinement SM of the denotational semantic (which could be also specified as a nondeterministic SM)

[tome]

> I was talking about FP there, and, I think, operational semantics must be embedded in FP.

I'm not sure what you mean. Are you claiming that operational semantics is somehow a poor fit for FP?

[pron]

I was being vague and imprecise. Obviously, any semantics is semantics of a language, and every language has its own perfectly fine operational semantics. I think that FP is a non-optimal first-choice for operational reasoning; let's call it that.

But while I have your attention, let me try to put the finger on two concrete problem areas in the typed-PFP reasoning. Now, I'm not saying that this isn't useful for a programming language; I am saying that it is a poor choice for reasoning about programs. I think that FP has to discrete "jumps" between modes, that are simply unnecessary for reasoning and do nothing but complicate matters (again, they may be useful for other reasons):

The first is the jump between "plain" functional code and monadic code. Consider an algorithm that sorts list of numbers using mergesort. The program could be written to use simple recursion, or, it could be written using a monad, with the monadic function encoding just a single step. Those two programs are very different but they encode the very same algorithm! The first may use a more denotational reasoning, and the second a more operational one. In TLA, there is no such jarring break. You can specify an algorithm anywhere you want on a refinement spectrum. Each step may be just moving a single number in memory, splitting the array, all the way to sorting in one step. The relationship between all these different refinement levels is a simple implication:

  R1 => R2 => R3

where R3 may be completely denotational and not even algorithmic as in:

   done => result = Sorted(input)

(assuming some operator Sorted).

The second discrete jump is between the program itself and the types. I was just talking to an Agda-using type theorist today, and we noted how a type is really a nondeterministic program specifying all possible deterministic programs that can yield it. This is a refinement relation. Yet, in FP, types are distinct from programs (even in languages where they use the same syntax). In TLA the relationship between a "type", i.e. a proposition about a program and the program is, you guessed it, a simple refinement, i.e. simple logical implication (he figures that intermediate refinement steps are analogous to a "program with holes" in Agda). So, the following is a program that returns the maximal element in a list:

A2 ≜ done = FALSE ∧ max = 0 ∧ [](done' = TRUE ∧ max' = Max(input))

but it is also the type (assuming dependent types) of all programs that find the maximum element in a list, say (details ommitted):

A1 ≜ done = FALSE ∧ max = {} ∧ i = 0 ∧ [](IF i = Len(input) THEN done' = TRUE ELSE (input[i] > max => max' = input[i]) ∧ i' = i + 1)

Then, A1 => A2, because A1 is a refinement of A2.

So two very central concepts in typed-PFP, namely monads and types are artificial constructs that essentially just mean refinements. Not only is refinement a single concepts, it is a far simpler concept to learn than either monads or types. In fact, once you learn the single idea of an "action" in TLA, which is how state machines are encoded as logic (it is not trivial for beginners, but relatively easy to pick up), refinement is plain old familiar logical implication.

So I've just removed two complicated concepts and replaced it with a simple one that requires little more than highschool math, all without losing an iota of expressivity or proving power.

[lmm]

You always want to decompose your model to help reasoning about it though, no? Even if you're modeling as a state machine, rather than a single big state machine you'd want to separate it into small orthogonal state machines as much as possible.

I see the functional approach as taking that one step further: separate the large proportion of the program that doesn't depend on state at all (i.e. that's conceptually just a great big lookup table - which is the mathematical definition of a function) from the operations that fundamentally interact with state. I find anything involving state machines horrible to reason about, so I'd prefer to minimize the amount of the program where I have to think about them at all.

[pron]

> Even if you're modeling as a state machine, rather than a single big state machine you'd want to separate it into small orthogonal state machines as much as possible.

Your notion of state is too specific for the theoretical meaning. Which function is executing in an FP computation is also state, as is the values of its arguments. Every software system is a single, possibly nondeterministic state machine. The decomposition you speak of is merely a decomposition of the state transition into various chunks (formulas). You can think of an abstract state machine as a single (pure) function -- just like a simple FP program -- except not a function from the input to the output, but a function from one state to the next (kinda like a state monad's monadic function, but expressed more naturally). One last complication is that it isn't quite a function but a relation, as you want to express the fact that your state machine may do one of several things in the next state, e.g. to describe different interleaving of threads, or even to model the user generating input.

Another thing you want to do is refinement and abstraction, i.e. specify your algorithm in different levels of detail (machines with more or fewer states) and show that the properties you want are preserved in the abstraction. Of course, you won't do that for something as simple as a sorting algorithm, but you also want to reason about large complex things, like an OS kernel or a distributed database.

So TLA simplifies things further by saying that the whole world is a single state machine, and your specifications are restricted views of that "world machine". This allows you to specify a clock with minute second hands, and another specification of a clock with just a minute hand, and then say that both are views of the same clock, with the first being just a more refined description of it than the first (this is a problem with multiple state machines, as one takes a step every second, and the other only every minute).

> I see the functional approach as taking that one step further: separate the large proportion of the program that doesn't depend on state at all (i.e. that's conceptually just a great big lookup table - which is the mathematical definition of a function) from the operations that fundamentally interact with state.

Again, what you consider state and the "abstract state" in abstract state machines are not quite the same. There is no such thing as a program that doesn't depend on state. Wherever there's a program, there's state. If you implement an algorithm that finds the maximum element in a list of numbers by a simple traversal in a PFP language and in an impure imperative language, the result would look very different, but the algorithm is the same, hence the state and the state transitions are identical.

That's the whole point in thinking of algorithms, not of code. I'd guess that this is what you do anyway -- regardless of the language. You don't necessarily always reach the same level -- e.g. once your complex distributed blockchain needs to find the maximal number in a list, you may go down to the index level in an imperative language, yet stop at the fold level in FP, and that's fine (you decide what an abstract machine can do at each step) -- but ultimately, at some point, you always think of your algorithm in the abstract -- you see it running in your mind -- rather than in linguistic terms, and that is the interesting level to reason about it. Forget about FSMs. What you imagine is pretty much the abstract state machine you can reason about mathematically. A mathematical state machine is simply a description of your program's steps (and every program is composed of steps) at any level of detail.

[lmm]

> That's the whole point in thinking of algorithms, not of code. I'd guess that this is what you do anyway -- regardless of the language. You don't necessarily always reach the same level -- e.g. once your complex distributed blockchain needs to find the maximal number in a list, you may go down to the index level in an imperative language, yet stop at the fold level in FP, and that's fine (you decide what an abstract machine can do at each step) -- but ultimately, at some point, you always think of your algorithm in the abstract -- you see it running in your mind -- rather than in linguistic terms, and that is the interesting level to reason about it. Forget about FSMs. What you imagine is pretty much the abstract state machine you can reason about mathematically.

This is a fascinating view, but no, I really don't. I think of the function that finds the maximal number in a list as a mathematical function (that is, "really" a set of pairs - a giant lookup table, that the implementing computer will have some tricks for storing in a finite amount of memory but those tricks are implementation details). I think of a function composed of two functions (when I think about it at all) as a bigger table (like matrix multiplication) - not as anything stateful, and not as anything involving steps. Like, if I think about 5 + (2 * 3), I think of that as 5 + 6 or 11, and sure you can model that as a succession of states if you want but I don't find that a helpful/valuable way to think about it. It seems like you want to think of the process of evaluation as first-class, when as far as I'm concerned it's an implementation detail; I see my program as more like a compressed representation of a lookup table than a set of operations to be performed.

[pron]

> I think of the function that finds the maximal number in a list as a mathematical function

My point is not how you would classify the object which is the program, but how you would go about programming it. You can think of it as a lookup table all you want, but you will define your algorithm as some state machine. Maybe looking for the maximum value is too simple, as the use of a combinator is too trivial, but think of a program that sorts a list. You can imagine the program to be a function if you like, but when you design the algorithm, there is no way, no how, that you don't design it as a series of steps.

> not as anything stateful

Again, don't conflate what programmers normally think of as "stateful" with the concept of a state in an abstract state machine.

> and sure you can model that as a succession of states if you want but I don't find that a helpful/valuable way to think about it.

I don't find it helpful, either, so I don't do it (more on that later). Whether you want to say that 5 + (2 * 3) in TLA is one step or several is completely up to you. TLA is not a particular state machine model like lambda calculus or a Turing machine, but an abstract state machine.

> It seems like you want to think of the process of evaluation as first-class, when as far as I'm concerned it's an implementation detail

You are -- and I don't blame you, it's hard to shake away -- thinking in terms of a language, which has a given syntax and a given semantics and "implementation". When reasoning about algorithms mathematically, there are no "implementation" details, only the level of the algorithm you care to specify. In TLA+ you can define higher-order recursive operators like "reduce" that does a reduction in one step -- or zero-time if you like -- or you can choose to do so over multiple steps. It is up to you. There is no language, only an algorithm, so no "implementation", only the level of detail that interests you in order to reason about the algorithm. Personally, for simple folds I just use the reduce operator in "zero-time", because I really don't care about how it's done in the context of my distributed data store. But if you wanted to reason about, say, performance of a left-fold or a right-fold, you'll want to reason about how they do it, and how any computation is done is with a state machine.

[pron]

Let me also address this with some snarky quotes by Lamport[1] (and just add, that having tried what he suggests on real-world programs, although what he says may sound more complicated, it is actually simple):

> Computer scientists collectively suffer from what I call the Whorfian syndrome — the confusion of language with reality

> [R]epresenting a program in even the simplest language as a state machine may be impossible for a computer scientist suffering from the Whorfian syndrome. Languages for describing computing devices often do not make explicit all components of the state. For example, simple programming languages provide no way to refer to the call stack, which is an important part of the state. For one afflicted by the Whorfian syndrome, a state component that has no name doesn’t exist. It is impossible to represent a program with procedures as a state machine if all mention of the call stack is forbidden. Whorfian-syndrome induced restrictions that make it impossible to represent a program as a state machine also lead to incompleteness in methods for reasoning about programs.

> To describe program X as a state machine, we must introduce a variable to represent the control state—part of the state not described by program variables, so to victims of the Whorfian syndrome it doesn’t exist. Let’s call that variable pc.

> Quite a number of formalisms have been proposed for specifying and verifying protocols such as Y. The ones that work in practice essentially describe a protocol as a state machine. Many of these formalisms are said to be mathematical, having words like algebra and calculus in their names. Because a proof that a protocol satisfies a specification is easily turned into a derivation of the protocol from the specification, it should be simple to derive Y from X in any of those formalisms. (A practical formalism will have no trouble handling such a simple example.) But in how many of them can this derivation be performed by substituting for pc in the actual specification of X? The answer is: very, very few.

> Despite what those who suffer from the Whorfian syndrome may believe, calling something mathematical does not confer upon it the power and simplicity of ordinary mathematics.

Obviously, we all suffer from the Whorfian syndrome, but I think it's worthwhile to try and shake it off. Luckily, doing it doesn't require any study -- just some thinking.

[1]: http://research.microsoft.com/en-us/um/people/lamport/pubs/d...

[jroesch]

I feel like you have confused quite a few concepts here.

First, type theory based proof assistants (like Coq) and model checkers are very different tools with very different guarantees. Most model checkers are used for proving properties about finite models and can not reason about infinite structures.

A type theory with inductive types gives one a mechanism for constructing inductive structures, and the ability to write proofs about them.

I am not sure how you have equated denotational semantics and type theory but there is no inherent connection, denotational semantics are just one way to give a language semantics. One can use them to describe the computational behavior of your type theory, but they are not fundamental.

Category theory and type theory have a cool isomorphism between them, but otherwise can be completely silo'd you can be quite proficient in type theory and never touch or understand any category theory at all.

On the subject of TLA+, Leslie Lamport loves to talk about "ordinary mathematics" but he just chose a different foundational mathematics to build his tool with, type theory is an alternative formulation in this regard. One that is superior in some ways since the language for proof, and programming is one and the same and does not require strange stratification or layering of specification and implementation languages.

Another issue with many of these model based tools is the so called "formality gap" building a clean model of your program and then proving properties is nice, but without a connection to your implementation the exercise has questionable value. Sure, with distributed systems for example, writing out a model of your protocol can help find design bugs, but it will not stop you from incorrectly implementing said protocol. In the distributed systems even with testing, finding safety violations in practice is hard, and many of them can occur silently.

Proof assistants like Coq make doing this easier since your implementation and proof live side by side, and you can reason directly about your implementation instead of a model. If you don't like dependently typed functional languages you can check out tools like Dafny which provide a similar work style, but with more automation and imperative programming constructs.

> This formulation serves as the basis for most formal reasoning of computer programs.

On this statement I'm not sure what community you come from, but much of the work going on in the research community is using things like SMT which exposes SAT and a flavor of first order logic, an HOL based system like Isabelle, or type theory, very few people use tools like set theory to reason about programs.

> Engineers, however, already have nearly all the math they need to reason about programs and algorithm in the common mathematical way (they just may not know it, which is why I so obnoxiously bring it up whenever I can, to offset the notion that is way overrepresented here on HN that believes that "PFP is the way to mathematically reason about programs".

Finally this statement is just plain not true, abstractly its easy to hand way on paper about the correctness of your algorithm. I encourage you to show me the average engineer who can pick up a program and prove non-trivial properties about its implementation, even on paper. I wager even proving the implementation of merge sort correct would prove too much. I've spent the last year implementing real, low-level systems using type theory, and this stuff is hard if you can show me a silver bullet I would be ecstatic, but any approach with as much power and flexibility is at least as hard to use.

Its not that "PFP" (and its not PFP, its type theory that makes this possible) is the "right way" to reason about programs but that it makes it possible to reason about programs. For example, how do you prove a loop invariant in Python? how would you even start? I know of a few ways to do this, but most provide a weaker guarantee then you would type theory version would, and requires a large trusted computing base.

[tome]

> > This formulation serves as the basis for most formal reasoning of computer programs.

> On this statement I'm not sure what community you come from, but much of the work going on in the research community is using things like SMT which exposes SAT and a flavor of first order logic, an HOL based system like Isabelle, or type theory, very few people use tools like set theory to reason about programs.

Pron is an engineer and he cares about what's easy for engineers to use. He's uninterested in research.

[pron]

Well, interested intellectually, but yes, I'm an engineer and I judge the utility of tools by their applicability to the work of engineers. That a tool like Coq could theoretically be used to fully reason about and verify a large program is of little interest to me, as no one so far has been able to do that, let alone enable engineers to do that.

BTW, I'm not too sure what the point on SMT was. SMT just uses SAT for whatever theory is needed (and possible). TLA+ uses SMT (as well as Isabelle) extensively as automated tactics in deductive proofs (in set theory), which I have only just started to use. SMTs (and certainly SATs) are not related in any way to one mathematical formulation or another, just like garbage collection isn't. In fact, SATs are commonly used today in bounded temporal-logic model checking.

[brohee]

Compcert doesn't qualify as a large program?

[tome]

> building a clean model of your program and then proving properties is nice, but without a connection to your implementation the exercise has questionable value

This is one thing that has always confused me about TLA+ since pron introduced me to it. Maybe translation of specification into implementation is always the easy part, though ...?

[pron]

> Maybe translation of specification into implementation is always the easy part, though ...?

Not only is it the easy part, but we're talking about reasoning about programs. If reasoning requires end-to-end certification you're working too hard.

Even within TLA+, you don't work at one refinement level. The lowest level is just too low, and therefore too hard for some proofs. You create a couple of sound refinements -- simpler state machines that capture the relevant essence -- and verify the behavior of yours. It's simpler than it sounds (refinement is just plain old logical implication, =>, in TLA) but it does take effort to reason about -- let alone prove -- a complex algorithm regardless of the formalism you use. FP doesn't make it easier, and it does require more difficult math.

[pron]

> First, type theory based proof assistants (like Coq) and model checkers are very different tools with very different guarantees.

I wasn't talking about model checkers but about mathematical specification languages. Some of them have proof assistants to help prove them, some have model checkers to help prove them, and some (like TLA+) have both. But the point is the formulation, not the proof tool.

> I am not sure how you have equated denotational semantics and type theory

No, I said FP is based on denotational semantics, and that reasoning about (typed) FP programs requires some type theory.

> but he just chose a different foundational mathematics to build his tool with

(When I speak of the math I'd rather refer to the logic TLA rather than the tool TLA+, but that doesn't matter). Obviously there is no objective, external way to classify mathematical theories as easy or hard. But that tool requires little more than highschool math, and it's been shown to be readily grasped by engineers with little training and almost no support. I think this qualifies as an objective evidence -- if not proof -- that it is, indeed, simpler, and the main reason why I encourage engineers to learn that first, and only later learn "FP math" if they wish.

> Proof assistants like Coq make doing this easier since your implementation and proof live side by side, and you can reason directly about your implementation instead of a model.

That "easier" bit is theoretical. AFAIK, there has been only one non-trivial real-world program written in Coq, it was written by a world-expert, it took a lot of effort in spite of being quite small, and even he had difficulties, so he skipped on the termination proofs.

> very few people use tools like set theory to reason about programs.

Don't use set theory if you don't want -- though it is easier by the measure I gave above -- as that's just the "static" part of the program. I'm talking about the dynamic part and temporal logic(s). TLs are far more common when reasoning about programs in the industry than any FP approach. Or even other approaches that work on Kripke structures, such as abstract interpretation.

> I encourage you to show me the average engineer who can pick up a program and prove non-trivial properties about its implementation, even on paper.

I'm one.

> I wager even proving the implementation of merge sort correct would prove too much.

I wager that I can take any college-graduate developer, teach them TLA+ for less than a week, and then they'd prove merge-sort all by themselves.

> I've spent the last year implementing real, low-level systems using type theory, and this stuff is hard

It is. But I've spent the past few months learning and then using TLA+ to specify and verify a >50KLOC, very complex distributed data structure, and Amazon engineers use TLA+ to reason about much larger AWS services every day. It's not end-to-end certified development, but reasoning and certified proof of implementation are two different things. State-machine reasoning is just easier, and it doesn't require the use of a special language. You can apply it to Python, to Java or to Haskell.

> but that it makes it possible to reason about programs

State machine and temporal logic approaches have made it possible to reason about programs so much so that thousands of engineers reason about thousands of safety-critical programs with them every year.

> For example, how do you prove a loop invariant in Python?

Python is not a mathematical formulation. But proving a loop invariant in TLA+ is trivial. Sure, there may be a bug in the tranlation to Python, but we're talking about reasoning not end-to-end certification, which is beyond the reach -- or the needs -- of 99.99% of the industry, and will probably stay there for the foreseeable future. The easiest way to reason about a Python program is to learn about state machines, and, if you want, use a tool like TLA+ to help you and check your work.

[tome]

> > I encourage you to show me the average engineer who can pick up a program and prove non-trivial properties about its implementation, even on paper.

> I'm one.

This doesn't seem right. TLA helps you prove properties of its specification, not its implementation. Or are you saying you can somehow prove properties of implementation too?

> I wager that I can take any college-graduate developer, teach them TLA+ for less than a week, and then they'd prove merge-sort all by themselves.

Sure, but prove properties of its specification, not its implementation. Unless I'm missing something ...

> State machine and temporal logic approaches have made it possible to reason about programs so much so that thousands of engineers reason about thousands of safety-critical programs with them every year.

Again, surely the specification of programs not their implementation?

> > For example, how do you prove a loop invariant in Python?

> ... Sure, there may be a bug in the tranlation to Python

Absolutely. That's the whole point. Translating it to Python is going to be hard and full of mistakes.

> but we're talking about reasoning not end-to-end certification, which is beyond the reach -- or the needs -- of 99.99% of the industry

If the implementation part truly is (relatively) trivial then this is astonishingly eye opening to me. In fact I'd say it captures the entire essence of our disagreements over the past several months.

> The easiest way to reason about a Python program is to learn about state machines, and, if you want, use a tool like TLA+ to help you and check your work.

No, that's the "easiest way" of reasoning about an algorithm that you might try to implement in Python.

[pron]

> TLA helps you prove properties of its specification, not its implementation. Or are you saying you can somehow prove properties of implementation too?

What do you mean by "implementation"? Code that would get compiled to machine code? Then no. But if you mean an algorithm specified to as low a level as that provided by your choice of PL (be it Haskell or assembly), which can then be trivially translated to code, then absolutely yes. In fact, research groups have built tools that automatically translate C or Java to TLA+, preserving all semantics (although, I'm sure the result is ugly and probably not human-readable, but it could be used to test refinement).

Usually, though, you really don't want to do that because that's just too much effort, and you'd rather stop at whatever reasonable level "above" the code you think is sufficient to give you confidence in your program, and then the translation may not be trivial for a machine, but straightforward for a human.

> Translating it to Python is going to be hard and full of mistakes.

Not hard. You can specify the program in TLA+ at "Python level" if you like. Usually it's not worth the effort. Now, the key is this: full of mistakes -- sure, but what kind of mistakes? Those would be mistakes that are easy for your development pipeline to catch -- tests, types whatever -- and cheap to fix. The hard, expensive bugs don't exist at this level but at a level above it (some hard to catch bugs may exist at a level below it -- the compiler, the OS, the hardware -- but no language alone would help you there.

But I can ask you the opposite question: has there ever been a language that can be compiled to machine code, yet feasibly allow you to reason about programs as easily and powerfully as TLA+ does? The answer to that is a clear no. Programming languages with that kind of power have, at least to date, required immense efforts (with the assistance of experts), so much so that only relatively small programs have ever been written in them, and at great expense.

So it's not really like PFP is a viable alternative reasoning to TLA and similar approaches. Either the reasoning is far too weak (yet good enough for many purposes, just not uncovering algorithmic bugs) or the effort is far too great. Currently, there is no affordable way to reason with PFP at all.

> No, that's the "easiest way" of reasoning about an algorithm that you might try to implement in Python.

You specify until the translation is straightforward. If you think there could be subtle bugs in the translation, you specify further. I'm in the process of translating a large TLA+ specification to Java. There's a lot of detail to fill in that I chose not to specify, but it's just grunt work at this point. Obviously, if a program is so simple that you can fully reason about it in your head with no fear of subtle design bugs, you don't need to specify anything at all...