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Readme.md

Mu: making programs easier to understand in the large

Imagine a world where you can:

  1. think of a tiny improvement to a program you use, clone its sources, orient yourself on its organization and make your tiny improvement, all in a single afternoon.

  2. Record your program as it runs, and easily convert arbitrary logs of runs into reproducible automatic tests.

  3. Answer arbitrary what-if questions about a codebase by trying out changes and seeing what tests fail, confident that every scenario previous authors have considered has been encoded as a test.

  4. Run first simple and successively more complex versions to stage your learning.

I think all these abilities might be strongly correlated; not only are they achievable with a few common concepts, but you can't easily attack one of them without also chasing after the others. The core mechanism enabling them all is recording manual tests right after the first time you perform them:

  • keyboard input
  • printing to screen
  • website layout
  • disk filling up
  • performance metrics
  • race conditions
  • fault tolerance
  • ...

I hope to attain this world by creating a comprehensive library of fakes and hooks for the entire software stack, at all layers of abstraction (programming language, OS, standard libraries, application libraries).

To reduce my workload and get to a proof-of-concept quickly, this is a very alien software stack. I've stolen ideas from lots of previous systems, but it's not like anything you're used to. The 'OS' will lack virtual memory, user accounts, any unprivileged mode, address space isolation, and many other features.

To avoid building a compiler I'm going to do all my programming in (virtual machine) assembly. To keep assembly from getting too painful I'm going to pervasively use one trick: load-time directives to let me order code however I want, and to write boilerplate once and insert it in multiple places. If you're familiar with literate programming or aspect-oriented programming, these directives may seem vaguely familiar. If you're not, think of them as a richer interface for function inlining.

Trading off notational convenience for tests may seem regressive, but I suspect high-level languages aren't particularly helpful in understanding large codebases. No matter how good a notation is, it can only let you see a tiny fraction of a large program at a time. Logs, on the other hand, can let you zoom out and take in an entire run at a glance, making them a superior unit of comprehension. If I'm right, it makes sense to prioritize the right tactile interface for working with and getting feedback on large programs before we invest in the visual tools for making them concise.

(More details.)

Taking Mu for a spin

First install Racket (just for the initial prototype; last tested with v6.3). Then:

  $ git clone https://github.com/akkartik/mu0
  $ cd mu
  $ git clone http://github.com/arclanguage/anarki
  $ cd anarki
  $   git checkout d7290130a7  # last compatible snapshot
  $ cd ..
  $ ./mu test mu.arc.t  # run tests

As a sneak peek, here's how you compute factorial in Mu:

  function factorial [
    ; allocate local variables
    default-space:space-address <- new space:literal, 30:literal
    ; receive inputs in a queue
    n:integer <- next-input
    {
      ; if n=0 return 1
      zero?:boolean <- equal n:integer, 0:literal
      break-unless zero?:boolean
      reply 1:literal
    }
    ; return n*factorial(n-1)
    tmp1:integer <- subtract n:integer, 1:literal
    tmp2:integer <- factorial tmp1:integer
    result:integer <- multiply n:integer, tmp2:integer
    reply result:integer
  ]

Programs are lists of instructions, each on a line, sometimes grouped with brackets. Each instruction operates on some operands and returns some results.

  [results] <- instruction [operands]

Result and operand values have to be simple; you can't nest operations. But you can have any number of values. In particular you can have any number of results. For example, you can perform integer division as follows:

  quotient:integer, remainder:integer <- divide-with-remainder 11:literal, 3:literal

Each value provides its name as well as its type separated by a colon. Types can be multiple words, like:

  x:integer-array:3  ; x is an array of 3 integers
  y:list:integer  ; y is a list of integers

Try out the factorial program now:

  $ ./mu factorial.mu
  result: 120  # factorial of 5
  ...  # ignore the memory dump for now

(The code in factorial.mu has a few more parentheses than the idealized syntax above. We'll drop them when we build a real parser.)


The name of a value is for humans, but what the computer needs to access it is its address. Mu maps names to addresses for you like in other languages, but in a more transparent, lightweight, hackable manner. This instruction:

  z:integer <- add x:integer, y:integer

might turn into this:

  3:integer <- add 1:integer, 2:integer

You shouldn't rely on the specific address Mu chooses for a variable, but it will be unique (other variables won't clobber it) and consistent (all mentions of the name will map to the same address inside a function).

Things get more complicated when your functions call other functions. Mu doesn't preserve uniqueness of addresses across functions, so you need to organize your names into spaces. At the start of each function (like factorial above), set its default space:

  default-space:space-address <- new space:literal, 30:literal

Without this line, all variables in the function will be global, something you rarely want. (Luckily, this is also the sort of mistake that will be easily caught by tests. Later we'll automatically generate this boilerplate.) With this line, all addresses in your function will by default refer to one of the 30 slots inside this local space.

Spaces can do more than just implement local variables. You can string them together, pass them around, return them from functions, share them between parallel routines, and much else. However, any function receiving a space has to know the names and types of variables in it, so any instruction should always receive spaces created by the same function, no matter how many times it's run. (If you're familiar with lexical scope, this constraint is identical to it.)

To string two spaces together, write one into slot 0 of the other. This instruction chains a space received from its caller:

  0:space-address <- next-input

Once you've chained spaces together, you can access variables in them by adding a 'space' property to values:

  3:integer/space:1

This value is the integer in slot 3 of the space chained in slot 0 of the default space. We usually call it slot 3 in the 'next space'. /space:2 would be the next space of the next space, and so on.

See counters.mu for an example of managing multiple accumulators at once without allowing them to clobber each other. This is a classic example of the sorts of things closures and objects are useful for in other languages. Spaces in Mu provide the same functionality.


You can append arbitrary properties to values besides types and spaces. Just separate them with slashes.

  x:integer-array:3/uninitialized
  y:string/tainted:yes
  z:list:integer/assign-once:true/assigned:false

Most properties are meaningless to Mu, and it'll silently skip them when running, but they are fodder for meta-programs to check or modify your programs, a task other languages typically hide from their programmers. For example, where other programmers are restricted to the checks their type system permits and forces them to use, you'll learn to create new checks that make sense for your specific program. If it makes sense to perform different checks in different parts of your program, you'll be able to do that.

To summarize: Mu instructions have multiple operand and result values. Values can have multiple rows separated by slashes, and rows can have multiple columns separated by colons. The address of a value is always in the very first column of the first row of its 'table'. You can visualize the last example above as:

  z           : list : integer  /
  assign-once : true            /
  assigned    : false

An alternative way to define factorial is by inserting labels and later inserting code at them.

  function factorial [
    default-space:space-address <- new space:literal, 30:literal
    n:integer <- next-operand
    {
      base-case:
    }
    recursive-case:
  ]

  after base-case [
    ; if n=0 return 1
    zero?:boolean <- equal n:integer, 0:literal
    break-unless zero?:boolean
    reply 1:literal
  ]

  after recursive-case [
    ; return n*factorial(n-1)
    tmp1:integer <- subtract n:integer, 1:literal
    tmp2:integer <- factorial tmp1:integer
    result:integer <- multiply n:integer, tmp2:integer
    reply result:integer
  ]

(You'll find this version in tangle.mu.)

This is a good time to point out that { and } are also just labels in Mu syntax, and that break and loop get rewritten as jumps to just after the enclosing } and { respectively. This gives us a simple sort of structured programming without adding complexity to the parser -- Mu functions remain just flat lists of instructions.


Another example, this time with concurrency.

  $ ./mu fork.mu

Notice that it repeatedly prints either '34' or '35' at random. Hit ctrl-c to stop.

Yet another example forks two 'routines' that communicate over a channel:

  $ ./mu channel.mu
  produce: 0
  produce: 1
  produce: 2
  produce: 3
  consume: 0
  consume: 1
  consume: 2
  produce: 4
  consume: 3
  consume: 4

  # The exact order above might shift over time, but you'll never see a number
  # consumed before it's produced.

  error - deadlock detected

Channels are the unit of synchronization in Mu. Blocking on channels are the only way tasks can sleep waiting for results. The plan is to do all I/O over channels that wait for data to return.

Routines are expected to communicate purely by message passing, though nothing stops them from sharing memory since all routines share a common address space. However, idiomatic Mu will make it hard to accidentally read or clobber random memory locations. Bounds checking is baked deeply into the semantics, and pointer arithmetic will be mostly forbidden (except inside the memory allocator and a few other places).

Notice also the error at the end. Mu can detect deadlock when running tests: routines waiting on channels that nobody will ever write to.


Try running the tests:

  $ ./mu test mu.arc.t
  $  # all tests passed!

Now start reading mu.arc.t to see how it works. A colorized copy of it is at mu.arc.t.html and http://akkartik.github.io/mu.

You might also want to peek in the .traces directory, which automatically includes logs for each test showing you just how it ran on my machine. If Mu eventually gets complex enough that you have trouble running examples, these logs might help figure out if my system is somehow different from yours or if I've just been insufficiently diligent and my documentation is out of date.

The immediate goal of Mu is to build up towards an environment for parsing and visualizing these traces in a hierarchical manner, and to easily turn traces into reproducible tests by flagging inputs entering the log and outputs leaving it. The former will have to be faked in, and the latter will want to be asserted on, to turn a trace into a test.

Credits

Mu builds on many ideas that have come before, especially:

  • Peter Naur for articulating the paramount problem of programming: communicating a codebase to others;
  • Christopher Alexander and Richard Gabriel for the intellectual tools for reasoning about the higher order design of a codebase;
  • Unix and C for showing us how to co-evolve language and OS, and for teaching the (much maligned, misunderstood and underestimated) value of concise implementation in addition to a clean interface;
  • Donald Knuth's literate programming for liberating "code for humans to read" from the tyranny of compiler order;
  • David Parnas and others for highlighting the value of separating concerns and stepwise refinement;
  • Lisp for showing the power of dynamic languages, late binding and providing the right primitives a la carte, especially lisp macros;
  • The folklore of debugging by print and the trace facility in many lisp systems;
  • Automated tests for showing the value of developing programs inside an elaborate harness;
  • Python doctest for exemplifying interactive documentation that doubles as tests;
  • ReStructuredText and its antecedents for showing that markup can be clean;
  • BDD for challenging us all to write tests at a higher level;
  • JavaScript and CSS for demonstrating the power of a DOM for complex structured documents.
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