Naming conventions in Standard ML

Many programming languages have a standard document that describes how to write and capitalise the names of functions, variables, and source files. It’s especially useful to have a standard for writing names made up from more than one word, where there are various options for how to join the words: “camel case”, which looks likeThis (with a capital letter “hump” in the middle), or “snake case”, which is underscore_separated.

I think Java in the mid-90s was the first really mainstream language to standardise file and variable naming conventions. The Java package mechanism requires files to be laid out in a particular way, and Sun published Java coding conventions which quickly became an effective standard for class and variable naming. Other languages followed. Python has had a standard that covers naming (PEP8) since 2001. More recent examples include Go and Swift.

Older languages tend to be less consistent. C++ is a mess: the standard library and most official example material uses snake_case for most names, but a great many developers, including those on most of the projects I’ve worked on, prefer camelCase, with capital initials for class names. File names are even more various: C++ source files are seen with .cpp, .cxx, .cc, and .C extensions; C++ header files with .h, .hpp, or no extension at all.

Standard ML (SML) is also a mess, and an interesting one because the language itself was standardised in 1990 and has been completely unchanged since the standard was revised in 1997. So although it is super-standardised, it’s a bit too old to have caught the wider shift in sentiment toward prescribing things like naming and file structure.

The SML standard is formal and very focused. It says nothing about coding style or naming, contains almost no examples using compound names, says nothing about filenames or file organisation, and specifies no way for one file to refer to another — the standard is indifferent to whether your source code is held in a file at all.

In trying to establish what naming conventions to use for my own code, I decided to look around at some existing libraries in SML to see what they had settled on.

The Basis library

SML has a standard library, the Basis library, which is a bit more recent than the language itself. Although it isn’t prescriptive, the library does use certain conventions itself and the introductory notes explain what they are. These cover only names of things within a program — not filenames, which are left up to the implementor of the standard. I’ll refer to them in the table below.

The Cornell style guide

Top search result for “SML naming conventions” for me is this online style guide for the Cornell CS312 course. It doesn’t cover file naming. Given the limited industry uptake for SML, an academic guide may be proportionately more influential than for other languages. I’ll mention this guide below as well.

Other code I looked at

I took a look at the following code:

  • The source of the MLton, MLKit, and SMLSharp compilers (excluding accompanying utility libraries)
  • The Basis library implementations shipped with MLton and SMLSharp
  • The SML/NJ extended library
  • The source of the Ur/Web language
  • The Ponyo library, an interesting fledgling effort to produce a broader base library than the Basis

In total, about 444,500 lines of code across 1790 SML source files. Some (presumably automatically-generated) source files are very long; while the mean file length is 248 lines including comments and blanks, the median is only 47.

Names within the language

The SML language has at least seven categories of things that need names: variables, type names, datatype constructors, exceptions, structures, signatures, and functors.

(By “variables” I really mean bindings, i.e. the vast majority of ordinary things with names: things that in a procedural language might include function names, variable names, and constant declarations. I’m using the word “variable” because it’s such a familiar everyday programming term.)

Source Variable Type name Datatype constructor Exception Structure Signature Functor
mlton variableName (mixed) DatatypeCtor ExceptionName* StructureName SIGNATURE_NAME FunctorName
mlkit (mixed) (mixed) DatatypeCtor* ExceptionName* StructureName SIGNATURE_NAME FunctorName
smlsharp variableName typeName* DATATYPE_CTOR* ExceptionName StructureName SIGNATURE_NAME FunctorName
basis variableName type_name DATATYPE_CTOR ExceptionName StructureName SIGNATURE_NAME FunctorName
smlnj-lib variableName type_name DATATYPE_CTOR ExceptionName StructureName SIGNATURE_NAME FunctorNameFn
urweb variableName type_name* DatatypeCtor ExceptionName StructureName SIGNATURE_NAME FunctorNameFn
ponyo variableName typeName DatatypeCtor ExceptionName Structure_Name SIGNATURE_NAME Functor_Name
cornell variableName type_name DatatypeCtor ExceptionName StructureName SIGNATURE_NAME FunctorName

* mostly

Here’s what I found, categorised into universal conventions, usual conventions, and “other”.

Universal

The following is the only universal convention:

Signature
SIGNATURE_NAME

The only code I found that doesn’t follow this convention is in the SML standard itself, which omits the underscore (like SIGNATURENAME).

Usual

The following conventions are not universal, but more popular than any other.

Variable Type name Exception Structure Functor
variableName type_name ExceptionName StructureName FunctorName

Camel case is clearly idiomatic for everything except type names. MLKit contains some snake-cased bindings as well, but none of the other libraries did. I like snake case in SML and I’ve written a fair bit of code using it myself; I hadn’t realised until now how uncommon it was. (It’s more common in SML’s sibling language OCaml. Ironic that, of the three very similar languages SML, OCaml, and F#, the only one not to use camel case is called OCaml.)

I spotted a handful of all-caps exception names and some camel case type names, but no library preferred those consistently.

The Ponyo library differs from the above for structures (Structure_Name) and functors (Functor_Name).

The SML/NJ library sort-of differs for functors, which are given a Fn suffix (FunctorNameFn). But you could think of this as part of the name, in which case the convention is the same.

Most type and datatype names used in public APIs are single words, or even single letters, so the convention often doesn’t matter for those.

Other

There seems to be no consensus about datatype constructors — I found DatatypeConstructor and DATATYPE_CONSTRUCTOR in roughly equal number.

Filenames

Nothing in the SML standard or Basis library cares about what source files are called, what file extension they use, or how you divide your code up among them. Some compilers might care, but most don’t. The business of telling the compiler which files a program consists of, or of expressing any relationships between files, is left up to external tools. SML has neither header files nor import directives.

This makes fertile ground for variety in naming schemes.

I’m going to consider only filenames that are associated with a primary structure, signature, or functor. Here’s the table.

Source Structure Signature Functor
mlton structure-name.sml signature-name.sig functor-name.fun
mlkit StructureName.sml SIGNATURE_NAME.sml* FunctorName.sml
smlsharp StructureName.sml SIGNATURE_NAME.sig* FunctorName.sml
mlton-basis structure-name.sml signature-name.sig functor-name.fun
smlsharp-basis StructureName.sml SIGNATURE_NAME.sig (none)
snlnj-lib structure-name.sml signature-name-sig.sml functor-name-fn.sml
urweb structure_name.sml signature_name.sig (n/a)
ponyo Structure_Name.sml SIGNATURE_NAME.ML Functor_Name.sml

* mostly

Clearly very inconsistent. There are no universal or usual conventions, only “other”.

Behind this there is a wider question about code organisation in files — should each signature live in its own file? Each structure? In many cases they do, but that is also far from universal.

If you use a scheme in which filenames are clearly derived from signature and structure names, does that mean you shouldn’t put more than one structure in the same file? What do you do with code that is not in any structure? Really it’s a pity to have to think about filenames at all, in a language that is so completely indifferent to file structure.

A Reasonable Recommendation

A plausible set of rules based on the above.

For names within the language:

Variable Type name Datatype constructor Exception Structure Signature Functor
variableName type_name DATATYPE_CTOR ExceptionName StructureName SIGNATURE_NAME FunctorName

This is the style used by the Basis library. Apart from datatype constructors, everything here was in the majority within the libraries I looked at.

For datatype constructors it seems reasonable to pick the most visible option and one that is consistent with the names in Basis. (This differs from the Cornell guide, however.) There is no confusion between these and signature names, because signature names never appear anywhere except in the declaration lines for those signatures and the structures that implement them.

For filenames:

Structure Signature Functor
structure-name.sml signature-name.sig functor-name.sml

The logic here is:

  • It’s still not a great idea to expect a case sensitive filesystem, so all-one-case is good
  • Generally use .sml extension for SML source
  • But the .sig extension for signatures seems very widely used, and it’s fair to make public signatures as easy to spot as possible
  • The .ml extension is not a great idea because it clashes with OCaml
  • The .fun extension used by MLton is a bit obscure, and you don’t always want to separate out functors (if you want to make functors more distinctive, give them names ending in Fn, as the SML/NJ library does).

 

F♯ has possibilities

A couple of months ago, Microsoft announced that they were buying a company called Xamarin, co-founded by the admirable Miguel “you can now flame me, I am full of love” de Icaza. (No sarcasm — I think Miguel is terrific, and the delightfully positive email linked above really stuck with me; if only I could have that attitude more often.)

As I understand it, Xamarin makes

  1. the Mono runtime, a portable third-party implementation of Microsoft’s .NET runtime for the C# and F# programming languages
  2. the eponymous Xamarin frameworks, which can be used with .NET to develop mobile apps for iOS and Android
  3. plugins for the Visual Studio IDE on Windows and the MonoDevelop IDE on OS/X to support mobile platform builds using Xamarin (the MonoDevelop-plus-plugins combo is known as Xamarin Studio).

Then a couple of days ago, the newly-acquired Xamarin declared

  1. that the Mono runtime was switching from LGPL/GPL licenses to MIT, allowing no-cost use in commercial applications
  2. that Microsoft were providing a patent promise (which I have not closely read) to remove concerns for commercial users of Mono
  3. that the Xamarin frameworks for iOS and Android, and the IDE plugins, were now free (of cost)
  4. that at some future point the Xamarin frameworks would be open sourced

I’m trying to unpick exactly what this could mean to me.

According to this discussion on Hacker News, the IDE plugins are remaining proprietary (which appears to mean that no IDE on Linux will be supported, since the IDE plugins are not currently available for Linux) but that “the Xamarin runtime and all the commandline tools you need to build apps” will be open sourced.

What this means

as I understand it,

  • Developers working on proprietary .NET applications will be able to build and release versions for other platforms than Windows, using Mono, at no extra cost
  • Developers working on open source .NET applications will be able to publish the ensemble with Mono under the MIT license if desired and will (apparently) be free of patent concerns
  • Developers will be able to make both proprietary and open source .NET applications for iOS and Android at no cost using Windows and OS/X
  • There is a possibility of being able to do builds of the above using Linux as well once the SDK is open, though probably without an IDE

Unrelatedly, there are separate projects afoot to provide native code and to-Javascript compilers for .NET bytecode.

What I’m interested in

I do a range of programming including a mixture of signal-processing and UI work, and am interested in exploring comprehensible, straightforward functional languages in the ML family (I wrote a little post about that here). Unlike many audio developers I have relatively limited demands on real-time response, but everything I write really wants to be cross-platform, because I’ve got specialised users on pretty every common platform and I have limited time and funding. (I understand that cross-platform apps are often inferior to single-platform apps, but they’re better than no apps.)

Xamarin doesn’t quite meet my expectations because it’s not really a cross-platform framework in the manner of Qt (which I use) or JUCE (which is widely used by others in my field). Instead of providing a common “widget set” across all platforms, Xamarin provides a separate thin interface to the native UI logic for each platform. It’s hard to judge how much more work this is, without knowing where the abstraction boundaries lie, but it may be a more relevant and sensible distinction on mobile platforms (where the differences are often in interaction and layout) than desktops (where the differences are mostly about how large numbers of individual widgets look).

An ideal combination of language and framework for me goes something like

  • strongly-typed, mostly functional, mostly immutable data structures
  • efficient unboxed support for floating-point vector types, including SIMD support
  • simple syntax (SML is nice)
  • low-cost foreign-function interface for C integration
  • high-level approach to multithreading
  • can work with gross UI layout in HTML5 (possibly DOM-update reactive UI style?)
  • good libraries for e.g. audio file I/O, signal processing, matrix algebra
  • can develop on Linux and deploy to all of Linux, Windows, OS/X, iOS, Android
  • free (or cheap, for proprietary apps) and open source (for open source apps)
  • has indenting Emacs mode

Where F# appears to score

F#, Microsoft’s ML-derived functional language for the .NET CLR, hits several of these. It has the typing, mostly-functional style, syntax, FFI, multithreading, libraries, deployment and licensing, and potentially the development platform (if the open source Xamarin framework should lead to the ability to build mobile apps directly from Linux).

I’m not sure about floating-point and vectors or about reusable HTML-style UI. I’d like to make the time to do another comparison of some ML-family languages, focusing on DSP-style float activity and on threading. I’ve done a bit of related work in Standard ML, which I could use as a basis for comparison.

Unless and until I get to do that, I’d love to hear any thoughts about F# as a general-purpose DSP-and-UI language, for a developer whose home platform is Linux.

My impression from the feedback on my earlier post was that the F# community is both enthusiastic and polite, and I notice that F# is the third most-loved language in the StackOverflow’s 2016 survey. Imagine a language that is useful no matter what platform you’re targeting, and whose developers love it. I can hope.

 

Fold: at the limit of comprehension

Fold” is a programming concept, a common name for a particular higher-order function that is widely used in functional programming languages. It’s a fairly simple thing, but in practice I think of it as representing the outer limit of concepts a normal programmer can reasonably be expected to grasp in day-to-day work.

What is fold? Fold is an elementary function for situations where you need to keep a tally of things. If you have a list of numbers and you want to tally them up in some way, for example to add them together, fold will do that.

Fold is also good at transforming sequences of things, and it can be used to reverse a list or modify each element of a sequence.

Fold is a useful fundamental function, and it’s widely used. I like using it. I just scanned about 440,000 lines of code (my own and other people’s) in ML-family languages and found about 14,000 that either called or defined a fold function.

Let me try to describe fold more precisely in English: It acts upon some sort of iterable object or container. It takes another function as an argument, one that the caller provides, and it calls that function repeatedly, providing it with one of the elements of the container each time, in order, as well as some sort of accumulator value. That function is expected to return an updated version of the accumulator each time it’s called, and that updated version gets passed in to the next call. Having called that function for every element, fold then returns the final value of the accumulator.

I tried, but I think that’s quite hard to follow. Examples are easier. Let’s add a list of numbers in Standard ML, by folding with the “+” function and an accumulator that starts at zero.

> val numbers = [1,2,3,4,5];
val numbers = [1, 2, 3, 4, 5]: int list
> foldl (op+) 0 numbers;
val it = 15: int

What’s difficult about fold?

  1. Fold is conceptually tricky because it’s such a general higher-order function. It captures a simple procedure that is common to a lot of actions that we are used to thinking of as distinct. For example, it can be used to add up a list of numbers, reverse a list of strings, increase all of the numbers in a sequence, calculate a ranking score for the set of webpages containing a search term, etc. These aren’t things that we habitually think of as similar actions, other than that they happen to involve a list or set of something. Especially, we aren’t used to giving a name to the general procedure involved and then treating individual activities of that type as specialisations of it. This is often a problem with higher-order functions (and let’s not go into monads).
  2. Fold is syntactically tricky, and its function type is confusing because there is no obvious logic determining either the order of arguments given to fold or the order of arguments accepted by the function you pass to it. I must have written hundreds of calls to fold, but I still hesitate each time to recall which order the arguments go in. Not surprising, since the argument order for the callback function differs between different languages’ libraries: some take the accumulator first and value second, others the other way around.
  3. Fold has several different names (some languages and libraries call it reduce, or inject) and none of them suggests any common English word for any of the actions it is actually used for. I suppose that’s because of point 1: we don’t name the general procedure. Fold is perhaps a marginally worse name than reduce or inject, but it’s still probably the most common.
  4. There’s more than one version of fold. Verity Stob cheekily asks “Do you fold to left or to the right? Do not provide too much information.” Left and right fold differ in the order in which they iterate through the container, so they usually produce different results, but there can also be profound differences between them in terms of performance and computability, especially when using lazy evaluation. This means you probably do have to know which is which. (See footnote below.)

A post about fold by James Hague a few years ago asked, “Is the difficulty many programmers have in grasping functional programming inherent in the basic concept of non-destructively operating on values, or is it in the popular abstractions that have been built-up to describe functional programming?” In this case I think it’s both. Fold is a good example of syntax failing us, and I think it’s also inherently a difficult abstraction to recognise (i.e. to spot the function application common to each activity). Fold is a fundamental operation in much of functional programming, but it doesn’t really feel like one because the abstraction is not comfortable. But besides that, many of the things fold is useful for are things that we would usually visualise in destructive terms: update the tally, push something onto the front of the list.

In Python the fold function (which Python calls reduce) was dropped from the built-in functions and moved into a separate module for Python 3. Guido van Rossum wrote, “apart from a few examples involving + or *, almost every time I see a reduce() call with a non-trivial function argument, I need to grab pen and paper to diagram what’s actually being fed into that function before I understand what the reduce() is supposed to do.” Instead the Python style for these activities usually involves destructively updating the accumulator.

Functional programming will surely never be really mainstream so long as fold appears in basic tutorials for it. Though in practice at least, because it’s such a general function, it can often be usefully hidden behind a more discoverable domain-specific API.

***

(Footnote. You can tell whether an implementation of fold is a left or right fold by applying it to the list “cons” function, which is often called “::”. If this reverses a list passed to it, you have a left fold. For example, the language Yeti has a function simply called fold; which is it? —

> fold (flip (::)) [] [1,2,3,4];
[4,3,2,1] is list<number>

So it’s a left fold.)

 

Perl 6

I see the official release of the Perl 6 language specification happened on Christmas day.

The first piece of commercial web development I did was in Perl 5. A lot of people can probably say the same thing. This one was a content-management system led by James Elson in 1999 at PSWeb Ltd, a small agency in Farringdon that renamed itself to Citria and expanded rapidly during 1999-2001 before deflating even more rapidly when the dotcom bust arrived.

My recollection was that this particular CMS only ever had one customer, the BBC, who used it only for their very small Digital Radio site. But I still have a copy of the code and on inspection it turns out to have some comments that must have been added during a later project, so perhaps it did get deployed elsewhere. It was a neat, unambitious system (that’s a good thing, James is a tasteful guy) that presented a dynamic inline-editing blocks-based admin interface on a backend URL while generating static pages at the front end.

I remember there was an open question, for a time, about whether the company should pursue a product strategy and make this first CMS, or something like it, the basis of its business, or else take up a project strategy and use whatever technology from whichever provider seemed most appropriate for each client. The latter approach won out. It’s interesting to speculate about the other option.

(I like to imagine that the release of Perl 6 is sparking tiresome reminiscences like this from ageing programmers across the world.)

Perl 6 looks like an interesting language. (It’s a different language from Perl 5, not compatible with it.) The great strength of Perl was of course its text-processing capacity, and for all the fun/cute/radically-unmaintainable syntax showcased on the Perl 6 site, it’s clear that that’s still a big focus. Perl 6 appears to be the first language to attempt to do the right thing with Unicode from the ground up: that is, to represent text as Unicode graphemes, rather than byte strings (like C/C++), awkward UTF-16 sequences (Java), or ISO-10646 codepoint sequences (Python 3). This could, in principle, mean your ad-hoc botched-together text processing script in Perl 6 has a better chance of working reliably across multilingual inputs than it would in any other language. Since plenty of ad-hoc botched-together text processing still goes on in companies around the world, that has the feel of a good thing.

Chordino troubles

On September the 9th, I released a v1.0 build of the Chordino and NNLS Chroma Vamp plugin. This plugin analyses audio recordings of music and calculates some harmonic features, including an estimated chord transcription. When used with Sonic Visualiser, Chordino is potentially very useful for anyone who likes to play along with songs, as well as for research.

Chordino was written by Matthias Mauch, based on his own research. Although I made this 1.0 release, my work on it only really extended as far as fixing some bugs found in earlier releases using the Vamp Plugin Tester.

Unfortunately, with one of those fixes, I broke the plugin. The supposedly more reliable 1.0 update was substantially less accurate at identifying both chord-change boundaries and the chords themselves than any previous version.

I didn’t notice. Nor did Matthias, who had recently left our research group and was busy starting at a new job. One colleague sent me an email saying he had problems with the new release, but I jumped to the completely wrong conclusion that this had something to do with parameter settings having changed since the last release, and suggested he raise it with Matthias.

I only realised what had happened after we submitted the plugin to MIREX evaluation, something we do routinely every year for plugins published by C4DM, when the MIREX task captain Johan Pauwels emailed to ask whether I had expected its scores to drop by 15% from the previous year’s submission (see results pages for 2014, 2015). By that time, the broken plugin had been available for over a month.

This is obviously hugely embarrassing—perhaps the most unambiguous screwup of my whole programming career so far. As the supposed professional software developer of my research group, I took someone else’s working code, broke it, published and promoted the broken version with his name all over it, and then submitted it to a public evaluation, again with his name on it, where its brokenness was finally made pointed out to me, a month later, by someone else. Any regression test, even on only a single audio file, would have shown up the problem immediately. Regression-testing this sort of software can be tricky, but the simplest possible test would have worked here. And a particularly nice irony is provided by the fact that I’ve just come from a four-year project whose aims included trying to improve the way software is tested in academia.

I’ve now published a fixed version of the plugin (v1.1), available for download here. This one has been regression tested against known-good output, and the tests are in the repository for future use. The broken version is actually gone from the download page (though of course it is still tagged in the source repository), to avoid anyone getting the wrong one by accident.

I’m also working on a way to make simple regression tests easier to provide and run, for the other plugins I work on.

That’s all for the “public service announcement” bit of this post; read on only if you’re interested in the details.

What was the change that broke it? Well, it was a change I made after running the plugin through the Vamp Plugin Tester, a sort of automated fuzz-testing tool that helps you find problems with your code. (Again, there’s an irony here. Using this tool is undoubtedly a good practice, as it can show up all sorts of problems that might not be apparent to developers otherwise. Even so, I should have known well how common it is to introduce bugs while fixing things like compiler warnings and static analysis tests.)

The problem I was trying to fix here was that intermediate floating-point divisions sometimes overflowed, resulting in infinity values in the output. This only happened for unusual inputs, so it appeared reasonable to fix it by clamping intermediate values when they appeared to be blowing up out of the expected range. But I set the threshold too low, so that many intermediate values from legitimate inputs were also being mangled. I then also made a stupid typo that made the results a bit worse still (you can see the change in question around line 500 of the file in this diff).

Note that this only broke the output from the Chordino chord estimator, not the other features calculated by NNLS Chroma.

A digression. An ongoing topic of debate in the world of the Research Software Engineer is whether software development resources in academia should be localised within research groups, or centralised.

The localised approach, which my research group has taken with my own position, employs developers directly within a research subject. The centralised approach, typified by the Research Software Development group at UCL, proposes a group of software developers who are loaned or hired out to research groups according to need and availability.

In theory, the localised approach can be simpler to manage and should increase the likelihood of developers being available to help with small pieces of work requiring subject knowledge at short notice. The centralised approach has the advantage that all developers can share the non-subject-specific parts of their workload and knowhow.

I believe that in general a localised approach is useful, and I suspect it is easier to hire developers for a specific research group than to find developers good enough to be able to parachute in to anywhere from a central team.

In a case like this, though, the localised approach makes for quite a lonely situation.

Companies that produce large software products that work do so not because they employ amazing developers but because they have systems in place to support them: code review, unit testing, regression tests, continuous integration, user acceptance tests.

But for me as a lone professional developer in a research group, it’s essentially my responsibility to provide those safety nets as well as to use them. I had some of them in place for most of the code I work on, but there was a big hole for this particular project. I broke the code, and I didn’t notice because I didn’t have the right tests ready. Neither did the researcher who wrote most of this code, but that wasn’t his job. When some software goes out from this group that I have worked on, it’s my responsibility to make sure that the code aspects of it (as opposed to the underlying methods) work correctly. Part of my job has to be to assume that nobody else will be in a position to help.

 

… and an FFT in Standard ML

While writing my earlier post on Javascript FFTs, I also (for fun) adapted the Nayuki FFT code into Standard ML. You can find it here.

The original idea was to see how performance of SML compiled to native code, and SML compiled to Javascript using smltojs, compared with the previously-tested Javascript implementations and with any other SML versions I could find. (There’s FFT and DFT code from Rory McGuire here, probably written for clarity rather than speed, plus versions I haven’t tried in the SML/NJ and MLKit test libraries.)

I didn’t get as far as doing a real comparison, but I did note that it ran at more or less the same speed when compiled natively with MLton as the Javascript version does when run in Firefox, and that compiling to JS with smltojs produced much slower code. I haven’t checked where the overhead lies.