## 19.7.20

### Modules, components and their limits

I read Clear explanation of Rust's module system. The TL;DR the module system in rust is a way to (somewhat) break/hide the link between what things are defined and what source files the things are defined in. A rust module provides control over visibility across source files and makes explicit how references reach across "modules".

The post starts with a request to ignore what "module" means in other languages, and this plunged me into thoughts on all the things that are called modules. It is somewhat sad but to be expected that a basic word like "module" (or "package") could never have a standard meaning, despite all these programming languages supposedly built while we have all this PL theory.

The more academic vocabulary has "module" as a language concept that comes with a scope and visibility, a way to bundle types and named values. It's somehwat independent of installation, deployment (the only touch point being support for separate compilation), but - to my knowledge - there is no talk of an "installed-at-a-version" module. So the rust module concept is pretty consistent with that. Arranging a set of modules that fit together is the programmer's problem, not the researcher's.

Alas, there is this thing that happens at the level of Rust crates/npm packages, and it is really a different problem. Nevertheless, those things are also called modules in some languages (C++20 modules, Java modules, go modules).

In Java, source files in the same directory make a Java package and they come with a way to control visibility. The association between files and packages is rigid. More recently, Java modules were added to express dependencies between modules, visibility of packages - something like a "packages of packages", but really more on the level of Rust crates.

I am reminded of "components" (if anybody remembers component-oriented programming) because deployment is part of this game - the notion of dependencies only makes sense in the context of some installation. But then components used to be about runtime services (in addition to code), object brokers and such and here we would actually like to talk about less dynamic things, just code and functionality in the form of source and/or libraries. Still, components are things that get assembled and exist at versions, so I'd say it's an appropriate, if old-fashioned name.

Now, such a (static) component would abstractly describe what npm packages or Rust crates achieve, and also form of Debian development packages (sigh) where you install a C library in binary form and its header files. That gives us even more of a twist now, which is that C++ 20 modules are really modules in the sense of achieving grouping for visibility and scope (check) but at the same time that whole notion has a huge impact on C++ since it should actually make it easier to deploy C++ components. A relevant quote from the C++20 link above:

After a module is compiled once, the results are stored in a binary file that describes all the exported types, functions and templates. That file can be processed much faster than a header file, and can be reused by the compiler every place where the module is imported in a project.

So while a C++20 module is language concept, it is foreseeable that one day all self-respecting newly developed libraries will be deployed as (installed at a version) modules. Oh well. There was really no reason to hope that a standard meaning for "modules" and the concept "above" could be justified in any way, across languages. At least, there remains an insight that it really makes sense to deal with the two levels separately.

I sure wish we could all call the installed-at-a-version thing "component", and the language concept "modules". Just two paragraphs on how that might sound:

Every language needs a module system: a way to deal with grouping, interfaces (in terms of making visible - "exporting" only the interface and keeping the rest internal). Including files was always bad and indicates the lack of a module system. It's when one needs to deal with independently developed, deployed and release units that there comes the problem of dependencies, evolution and versions, and these things are the components. Components are related to modules in that they need to contain at least one of them for talking about the things that are provided and brought in scope.

If there is a dependency between versions of components and there are backwards incompatible (!) changes in the depended-upon component (or a left-pad situation), then this is something that needs to be dealt through changes to the depending component. That isn't something that module systems (or any other language design) can help much with.

## 18.7.20

### Relating partial evaluation, multi-stage programming and macros

Here is some quick fact on two metaprogramming techniques. $$\begin{array}{l} \mathsf{letrec}\ \mathtt{pow}\ =\ \lambda n : \mathsf{Int}. \lambda x : \mathsf{Int}. \\ \quad \mathsf{if}\ (\mathsf{eqInt}\ n\ 0)\ 1\ (x\ * (\mathtt{pow}\ (n - 1)\ x)) \end{array}$$

What can we do with an expression $\mathtt{pow}\ 3$?

## Partial evaluation

The "partial evaluation" answer is to inline the definition, and evaluate what can be evaluated, yielding a residual program. $$\begin{array}{l} \lambda x : \mathsf{Int}. \mathsf{if}\ (\mathsf{eqInt}\ 3\ 0)\ 1\ (x * (\mathtt{pow}\ (3 - 1)\ x)\\ \lambda x : \mathsf{Int}. x * (\mathtt{pow}\ 2\ x)\\ \ldots\\ \lambda x : \mathsf{Int}. x * (x * (x * 1)) \end{array}$$

## Staging

The multi-stage programming answer (or staging) is to rethink $\mathtt{pow}$ as a transformation of code. $$\newcommand{\qquote}[1]{\color{blue}{[|}#1\color{blue}{|]}} \begin{array}{l} \mathsf{letrec}\ \mathtt{pow}\ =\ \lambda n : \mathsf{Int}. \lambda x : \mathsf{\color{blue}{Code[Int]}}. \\ \quad \mathsf{if}\ (\mathsf{eqInt}\ n\ 0)\ \color{blue}{[|}1\color{blue}{|]}\ \color{blue}{[|}(x * (\mathtt{pow}\ (n - 1)\ x)\color{blue}{|]} \\ \mathsf{let}\ \mathtt{mkpow}\ =\ \lambda n : \mathsf{Int}. \qquote{\lambda y : \mathsf{Int}. (\mathtt{pow}\ n \qquote{y})}\\ \end{array}$$

The notation was (probably) not designed to scare you:

• $\qquote{\ldots}$ is quasi-quotation. What is between these brackets is code
• $\$(\ldots)$is used within a quasi-quoted expression to splice code. Now$\mathtt{mkpow}\ 3$is going to give us the code of the desired power function: $$\begin{array}{l} \qquote{\lambda y : \mathsf{Int}. (\mathtt{pow}\ 3\ \qquote{y})}\\ \qquote{\lambda y : \mathsf{Int}. (\mathsf{if}\ (\mathsf{eqInt}\ 3\ 0)\ \qquote{1}\ \qquote{(\qquote{y} * (\mathtt{pow}\ (3 - 1)\ \qquote{y})})}\\ \qquote{\lambda y : \mathsf{Int}. \qquote{(\qquote{y} * (\mathtt{pow}\ 2\ y)})}\\ \qquote{\lambda y : \mathsf{Int}. \qquote{y} * (\mathtt{pow}\ 2\ y)}\\ \qquote{\lambda y : \mathsf{Int}. y * (\mathtt{pow}\ 2\ y)}\\ \qquote{\ldots}\\ \qquote{\lambda y : \mathsf{Int}. y * (y * (y * 1))} \end{array}$$ There can be nested levels of$\color{blue}{\mathsf{Code}}$and this explains the term multi-stage programming. For objects of this type to be really useful, there should be a method$\mathsf{run}: \color{blue}{\mathsf{Code}[T]} \rightarrow T$. Macros are very related to the above: a macro gives the programmer a way to give a definition that is elaborated at compile time. This is similar to what is going on in splicing. The special case is that it is the compiler that calls$\mathsf{run}\$; in other words,the context in which a macro is elaborated is the "compile-time stage". The design of Scala's metaprogramming facilities is consistent with all this, and I picked this up from "A practical unification of multi-stage programming and macros" by Stucki, Biboudis, Odersky.

That's all; this is shallow as all my posts and there would be more to say; "Modal analysis of staged computation" by Davies and Pfenning would be a good place to continue for the curious and/or impatient. I'd like to connect these views on staging and macros and modal logic, but that's for another day.

## 4.7.20

### Language, Logic and Modeling

Language is the parrot of thought, and a hard to teach one, nothing more. („Sprache ist der Papagei des Gedankens, und ein schwer gelehriger, nichts weiter.“) ― Friedrich Hebbel

Here's a short note about a perspective on logic for informatics, and decidedly not mathematics.

Formal logic has its roots in formalizing mathematics. Expressing the steps of reasoning in a formal system would assure us that there are no "gaps". I wrote a brief account on the beginnings of formal logic elsewhere.

The 20th century brought with it the advent of machines for information processing. This has gone way beyond mathematics already. In informatics (a better word for "computer science") one deals with information processing, and its practical kin, software engineering, deals with systems of all kinds where information gets in contact with reality.

There is a view that holds that we can only understand reality through models (see constructivist epistemology). Now, whether one subscribes to this or not, it is obvious that informatics relies on models of reality; information itself is always part of some model of reality. If you worked with customers or product managers, you know it is shaky and always incomplete series of models of reality, always adapting to new circumstances.

All I am saying is: formal logic can and should help us as a tool for formulate precisely these models. Like in mathematics, we do not want to have "gaps" in our reasoning. This is a somewhat ambitious direction, as software systems are not always built according to specification, and specifications are usually prose. And it is not only about modeling the domain.

"Functional programming" has gathered a large following in recent years, and I think this is because less because "software should be mathematics", but programmers benefit from constructing their code according to models that leaves no "gaps". The tool at hand, a (statically typed) functional programming languages, forces one to think thoroughly about data and the operations that are going to be carried out.

One does not need to resort to philosophy for challenging this. Large software systems are distributed over many machines, and gives rise to challenges that require methods different from those that are mathematics (and different from programs that run on a single machine). While functional requirements lead us to modeling domain concepts, designing a system that meets those is constrainted by non-functional requirements. We need to consider of performance and machine capacity, and evaluate which underlying technologies to build on, what guarantees they provide. It is here that engineering can be meaningfully distinguished from programming, and work is influenced less by theorems, more by best practices and experience.

Engineering and domain modeling are full of abstract models (the favorite tool being: diagrams of boxes with their meaning provided by handwaving). Formal logic and actual, manifest models where people could experiment are not encountered very often, yet expressing these models, reasoning with them and testing/verifying them would be critical since mistakes are costly. Moreover, a large number of people have to understand and act in accordance to these models. Often, the only time where this becomes obvious are the APIs between subsystems, a point where much modeling has already happened and cannot be changed.

I will go further and claim that Conway's Law is a consequence of each team forming their own "unit of modeling". It would be a lot better when we learnt to communicate explicitly and precisely what our models are, explain our needs to model things differently and how we can translate between our different models instead of insisting on some "right" way of seeing something. I place my hopes in logic as a language and tool to do just that. Mathematics is a discipline where the practice of modeling a mathematical "universe" has advanced very far, and it is no wonder that people seeking to use precise methods turn to it. It is a mistake to interpret this field of application of formal logic as some sort of mathematical monopoly. Language is a tool to communicate, a logic is a style of language.

## 2.7.20

### I don't know how to Semantics

I missed out my daily writing practice of bad, unfinished, unedited thoughts. So why not combine it with idle thoughts on logic. The title alludes to the friendly LOGO error message, and this exchange on Twitter:

Andrej is not wrong, but axiomatic semantics is a thing in PL. So I'm collecting now the two different contexts for the word and idea of "semantics", the mathematical logic, one in programming languages. I am generally trying to embrace multitude of meanings in different contexts, but given the strong connections between PL and logic, it is worth pondering what is going on here.

# Linguistic meaning: the writing on the wall

The word "semantics" comes from σῆμα "sign", "marking" and refers to the study of meaning (somewhat associated with linguistics, the study of language). Now meaning a very broad thing to study, and how we create meaning and interpret signs proper (beyond language) leads us to semiotics. I promise I am not going to spiral into ever higher level of abstractions and get back to the more mundane divergences of academic disciplines; still I want to share a story to show the depth of the question "how we make meaning of something" and what meaning really is.

Here is an example of linguistic and semiotic meaning. I believe I had heard and understood in English the idiom "the writing is on the wall" long before I heard the biblical story. It is hard to tell, since I do not remember. Maybe I looked up the German word "Menetekel" – which is the same but doesn't sound as cool and portentous – and then forgot about it. Maybe I learned and chose to forget biblical reference. English is full of references to stories that become idioms and detached from their origins (I also learned very recently that "drinking the Kool-Aid" originates from a Jonestown massacre), yet somehow they convey meaning.

Conveying meaning in written language can only happen through more written language; however, language is definitely more than what is written. I claim it's possible that even if you never heard this story from the Bible and have no access to it, you can nevertheless learn the full meaning of the saying "the writing is on the wall" when you witness it being used in some situation that discusses the terrible ending to come. Whether that is from the textual, narrative context, or rather from the faces, gestures of the participant or from your understanding of the reality of the thing it is said in reference to, that's hard to tell.

Kamus writes how many meanings each word has, then, I wondered and tried to write down a Kamus showing, to how many words a meaning corresponds. (Bediuzzaman Said Nursi)
Wisdom is a tree which sprouts in the heart and fruits on the tongue. (Ali ibn Abi Talib)

Now, back to our divergent use of "semantics" in mathematical logic and programming languages.

# Semantics in mathematical logic

The predominant view in mathematical logic today is Tarski's conception of semantics and truth, adapted to model theory. This is what is taught to undergrads, and I shall like to deconstruct the assumptions that come with it:

• Statements are either true or false, or otherwise put, there is an objective notion of truth that we try to capture with logic languages.
• The meaning for a logical model is given by describing the interpretation of the language in a model
• The way we communicate or describe a model and the interpretation are well understood.

All issues one may have are subdued with the last point. You cannot really question why models are given in "informal set theory" because the whole meaning exercise requires some common ground. If one ever wonders how to give meaning to those things that are supposed to provide the well-understood meaning, one finds that formal set theory is relatively disconnected from what one is trying to do (and turns to type theory).

I find it very striking that at the time Tarski formulated his original criterion for defining truth, the lingua franca was higher-order logic (which is equivalent to simple type theory), not set theory. I do feel something was lost there. Classical higher-order logic, we are concerned with mapping to truth values, but point being about the mappings (not the "sets"). So rather than being the "informal set", a grouping of objects by some property, or a formal set which described by an obscure string formal set theory, a "higher-order logic" set is defined by its mapping. A "woman" is defined by a mapping

woman: Individuals -> {true, false}

... and you can argue when it returns true. I find this closer to discussions on meaning in the linguistic sense.

# Semantics in programming languages

In programming languages, "semantics" refers to the "meaning" to programming language (this is actually a surprising and confusing statement). Now, "meaning" is something a lot narrower than linguistic meaning.

• Something does not have to be universally and objectively true to give "meaning"
• The conventions we can rely on when giving meaning are entirely different.
• The way we communicate or describe a model and the interpretation are well enough understood.
• "Meaning" can be something like a specification that clarifies what statements and expressions of the language do. In practice, this is all informal. In research, there are three "styles":

• Denotational semantics. We choose to embrace the conventions of mathematics as our medium for conveying meaning as objective truth
• Operational semantics. The meaning is in the behavior, the execution of the programs. See what this interpreter does? That is the meaning.
• Axiomatic semantics. The meaning of a program is properties you can state.

This distinction is entirely standard and helpful, I learnt these as undergrad and e.g. Benjamin Pierce "Types and Programming Languages". It is also largely ignored in industry, where a natural languages specification relying on conventions is all you get for "meaning" (and stackoverflow may cover the gaps).

# Finale

Programming actually has its roots in formal logic. It is not a coincidence that the act of setting up formal languages and the burden of having to define meaning is shared by people who mathematical logic (ok, I should say formal logic to not exclude the philosophers) and programming languages, and that informaticians take an interest in proof assistants and machine-checked proof.

Observe that all styles leave open a path to full formalization, but for axiomatic semantics it is somewhat odd since we'd have to formalize logic in logic. The boundaries are also not as clear-cut, since one can also describe an interpreter by some mathematical structure (labeled transition systems) and there is als formal methods, verification and the "theory of a program" and how it needs some semantics to build on (not necessarily "the" semantics). The taxonomy is still very useful, though, since it says something about the intention.

My personal hobby is to study stacking systems of logic and reasoning, "compositional". Category theory is useful here, precisely because it permits to consistently ignore what is the foundation, and describe everything in terms of mappings (arrows), just like higher-order logic. I shall leave this topic for another post.