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. Build first simple and successively more complex versions of a program so you can 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
• 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.

Taking mu for a spin

Prerequisites: Racket from http://racket-lang.org

  $ cd mu
  $ git clone http://github.com/arclanguage/anarki

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

  def factorial [
    ; allocate some space for local variables
    default-scope/scope-address <- new scope/literal 30/literal
    ; receive args from caller in a queue
    n/integer <- arg
    {
      ; if n=0 return 1
      zero?/boolean <- eq n/integer, 0/literal
      break-unless zero?/boolean
      reply 1/literal
    }
    ; return n*factorial(n-1)
    tmp1/integer <- sub n/integer, 1/literal
    tmp2/integer <- factorial tmp1/integer
    result/integer <- mul tmp2/integer, n/integer
    reply result/integer
  ]

Programs are lists of instructions, each on a line, sometimes grouped with brackets. Instructions take the form:

  oargs <- OP args

Input and output args have to be simple; no sub-expressions are permitted. But you can have any number of them. In particular, instructions can return multiple output arguments. For example, you can perform integer division as follows:

  quotient/integer, remainder/integer <- idiv 11/literal, 3/literal

Each arg can have any number of bits of metadata like the types above, separated by slashes. Anybody can write tools to statically analyze or verify programs using new metadata. Or they can just be documentation; any metadata the system doesn't recognize gets silently ignored.

Try this program out now:

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

(The code in factorial.mu looks different from the idealized syntax above. We'll get to an actual parser in time.)

---

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

  def factorial [
    default-scope/scope-address <- new scope/literal 30/literal
    n/integer <- arg
    {
      base-case
    }
    recursive-case
  ]

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

  after recursive-case [
    ; return n*factorial(n-1)
    tmp1/integer <- sub n/integer, 1/literal
    tmp2/integer <- factorial tmp1/integer
    result/integer <- mul tmp2/integer, n/integer
    reply result/integer
  ]

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

---

Another example, this time with concurrency.

  $ ./anarki/arc mu.arc 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:

  $ ./anarki/arc mu.arc 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.

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).

---

Try running the tests:

  $ ./anark/arc 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.