End Point Blog: HGCI Summit Conference 2017, Malaysia: A conference on cloud, security and big data

I was asked by a friend to give a talk in the HGCI Summit conference on November 28th, 2017. This conference is meant to bridge the academic world and industry via knowledge and experience sharing, focusing on big data and cloud topics. It took place in the Center of Advanced Professional Education (CAPE), a center under the Universiti Teknologi Petronas (UTP) which has its main campus in Tronoh, Perak, Malaysia.

I will highlight several tracks which I attended.

Forum

On the first day, several faculty members sat together within a forum in which they discussed the main issues academics face when they need access to high performance computing. An audience member shared her experience completing her research group’s work which took very long to be rendered, while she could do it in a day when she submitted the work in a university in the US. One of the forum’s members then replied she could always work collaboratively with the other universities, and he (the forum member) offered his university’s facilities to be used for her research. Inter-varsity network bandwidth was also discussed in the forum.

Dr. Izzatdin

An interesting talk which I attended was delivered by Dr. Izzatdin from UTP. He shared his work on cloud-based crude oil refinery monitoring. The monitoring system web page is hosted on Microsoft’s Azure, where it will display the oil refinery data which were gathered batch by batch from the sensors. Metal corrosion is among the things monitored by the systems.

Mr. Aizat

I also attended a talk delivered by a friend of mine, Mr. Aizat from Informology. He shared the use of OpenStack for the provision of highly parallel computing instances. I saw that Aizat also used ready-made Ansible scripts in order to get the computing instances ready. By using Horizon (OpenStack’s user interface) it seems we could speed up the process of the instance provisioning. Aizat also shared a link for anyone interested to try out the “vanilla” version of OpenStack at TryStack. Another tool which he shared is ElastiCluster, a set of Python scripts which allow the user to create, manage, and set up cloud infrastructures using Ansible. Aizat also showed the use of Jujucharms, a portal which handles Juju, an open source application modelling tool developed by Canonical (the maker of Ubuntu Linux).

My talk

My talk was shortly after Aizat’s slot. I covered the use of algorithms to detect the similarity subsequence of patterns between malware and non-malware (benign software) and between the malware family variants. I obtained the malware samples from a Windows malware researcher when he did his PhD study and another dataset from a research unit in Asia. (Please contact me if you want these samples.)

These datasets contain either the Application Programming Interface (API) calls only, or the API calls with their arguments. These API calls were obtained by running the malware within a safe, virtualized environment and the calls were dumped into text files.

Since procedural language code commonly runs from top bottom, the API calls which were generated inherit the same idea, making them have the same structures if they share a common root. I used n-gram and Longest Common Subsequence (LCS) to perform the experiment. These two algorithms are categorized under the dynamic programming algorithms group, in which they created a subsequence from the sequence of problem for a pattern matching.

I also mentioned the use of Python (for example with scikit-learn) and Weka if we want to work on the problem using machine learning methods.

If you are interested, check out my PDF slides, Machine Learning Applications for the Cyber Security Threats.

Perl Hacks: Regenerating Perl School

About five years ago I ran a few training courses under the Perl School brand. The idea was simple – if you price training courses cheaply and run them at the weekend then you eliminate the most common reasons why people don’t keep their Perl knowledge up to date.

Of course, it’s not quite that simple. And I think I ran six courses before running out of attendees.

But there are still people who would benefit from getting some more up to date information about how Perl works. So I’ve decided to resurrect the Perl School brand in a new attempt to spread the Modern Perl knowledge beyond the echo chamber. I announced my plans during my lightning talk at last month’s London Perl Workshop.

This time I’m going to do it by publishing cheap books. You might remember that time I promised to write a guide to modern web development with Perl and how badly that ended up. But in the process, I learned a lot about publishing ebooks to Amazon. I even gave a talk where I suggested that Perl book publishing could become a cottage industry. And that’s what I’m currently aiming at.

I’ve made a start already. just before the LPW I published a book called Perl Taster which aims to take people through their first two hours of learning Perl. It’s cheap enough (and small enough) that people can give Perl a try without investing too much money or time.

But my plans don’t stop there. I have ideas for half a dozen other books that I can publish over the next few months. Basically, if you’ve one of my training courses over the last five years then you can expect a (short!) book based on that course to appear at some point during 2018. Currently my plans include books on:

  • Moose
  • DBIx::Class
  • Modern Core Perl
  • Dancer2
  • Testing

Obviously, there are plenty of other books that could be written this way. And I don’t want to have to write them all myself. Which is where you come in. Is there a Perl-related subject that you’re an expert on? Would you be interested in writing a book about it?

I’m offering to help people publish Perl books. If you can write a book using Markdown, then let me take care of the complicated bits of turning your text into an e-book and getting it published on Amazon (and, perhaps later, other e-book platforms).

So, over to you. What do you want to write a book about.

p,s. At some point I should probably finish the e-book I was writing about publishing e-books.

The post Regenerating Perl School appeared first on Perl Hacks.

Sawyer X: Perl 5 Porters Mailing List Summary: December 6th-10th

Hey everyone,

Following is the p5p (Perl 5 Porters) mailing list summary for the past week.

Enjoy!

Edit: Perl #132548 (Possible memory leak in S_regclass) was not rejected. It was fixed.

December 6th-10th

News and Updates

Ævar Arnfjörð Bjarmason created a proposed (RFC: Adding a perldisthist POD page) POD file for the various versions of Perl included in each distribution version.

Dave Rolsky shares ("Vulnerability" in Perl in the news) that Perl appears in the news because of a module that evals input.

Grant Reports

Issues

New Issues

Resolved Issues

Rejected Issues

Suggested Patches

A patch was provided by Chris to expand perldata regarding list assignments in Perl #132538.

Perl Foundation News: Maintaining the Perl 5 Core (Dave Mitchell): Grant Report for Nov 2017

This is a monthly report by Dave Mitchell on his grant under Perl 5 Core Maintenance Fund. We thank the TPF sponsors to make this grant possible.

I spent last month mainly:

* Fixing some issues thrown up by the new OP_MULTICONCAT optimisation;

* Fixing some regressions in deparse testing: 't/TEST -deparse' feeds every
  test suite script through the deparser before trying to run it; about 6
  scripts that formerly passed had started failing. I fixed all these, and
  removed another 6 or so scripts from the 'expected to fail' exclusion
  list.

SUMMARY:
      1:30 RT #132152 Bleadperl breaks SARTAK/NetHack-Item-0.21.tar.gz
      1:40 RT #132187 heap-buffer-overflow in Perl_fbm_instr
      2:20 RT #132385 BBC: PDL-2.018 affected too
      6:47 RT #132385 BBC: Whatever-0.23 affected too
      2:30 RT #132385 OP_MULTICONCAT breaks Bit-Vector-7.4
      2:26 Smoke FAIL's for lib/perl5db.t
      1:20 [perl #132430] Multiconcat breaks AIX builds
      1:51 fix OP_MULTICONCAT int type issues
     16:52 fix TEST -deparse regressions
      1:08 fix ext/B/t/optree_specials.t
      8:28 process p5p mailbox
      3:22 review security tickets
    ------
     50:14 TOTAL (HH::MM)

 215.6 weeks
2873.4 total hours
  13.3 average hours per week

There are 260 hours left on the grant.

Perl Foundation News: Call for Volunteers

Help needed for the 2018 TPC::NA

The Perl Conference in America, formerly YAPC::NA, has put forward a formal call for volunteers to help out with next year's event. Perl has likely helped you, we could even say it has been helping you to pay your bills? This may be your chance to give back to the community that isn't writing code.

TPC::NA 2018 (formerly known as YAPC::NA) is looking for volunteers to organize the conference. Can you do any of these?


  • Design logos & other creative things.

  • Draft announcements & other communication.

  • Be awesome & generally helpful.


What will you get in return?
The main benefit is in knowing you helped bring a well loved conference to life and the grateful thanks of all the attendees, however there will be other benefits given such as a staff conference clothing, drinks and other appropriate reward.

If the answer is yes, then please fill in this form: https://docs.google.com/forms/d/1VLzH7cgMtX5P5q0y5pykiPW19TxEfIb_LLBXAvq8c2c/vewform?edit_requested=true

Note
In person attendance is optional (but we would love to see you in person), though it will require consistent monthly and weekly web conferences with other organizers.

Perl Foundation News: Grant Report : Complete YAML::PP - November 2017

Tinita, the one-woman YAML ecosystem, continues apace with her grant Complete YAML::PP. Find her latest report on her blog.

She is making headway on some of the cooler features of YAML: flow style, anchors, and special tags. Legit boolean behavior is now implemented in YAML::XS. The blog post provides a nice rundown of the 4 ways to quote in YAML.

Wondering what all the fuss is about? check out Tina's London talk.

Code is at https://github.com/perlpunk/YAML-PP-p5.

MAJ

Eric Johnson (kablamo): I wrote a Perl book

I wrote a Perl book accidentally during my vacation. I started typing and everything just fell out of my head over the next few days. Its still pretty rough but I think its a decent start.

The book is called Minimum Viable Perl.

Ok, its not a real published book. Maybe I’ll self publish it on Amazon some day. For now its just a website. I’m going to call it a book anyway.

What its about

This book is for developers who want to get up to speed with Perl quickly through concise tutorials (about 1 screenful in size).

Being concise is one of the primary goals. In the age of stackoverflow and blogs and info graphics everyone is in a hurry. People shouldn’t have to wade through unnecessary prose. I literally review each sentence in each article and try to remove unnecessary words.

In order to keep things short and to the point I’ve also chosen to be opinionated and intentionally left out some dicussions and edge cases that were uncommon or not essential. Instead I’ve tried to link to more information.

Why I wrote it

I keep meeting good developers who are visiting Perl from other languages who are strugglng with the language. I’ve tried pointing them at various books and resources but that doesn’t seem to be enough. When I talk to them about their struggles, the top 3 problems I hear about are:

  1. Dereferencing (confusing)
  2. Object oriented programming (how?)
  3. Random stuff they can easily do in their favorite language but don’t yet know how to do in Perl (opening files, testing, templates, dependencies, etc).

My theory is these all boil down to the fact they don’t have a good, concise, easily digestable online source of information and answers in one easy to find location. The information is out there but its not easy for new people to find. This is my attempt to solve that problem.

The future of Minimum Viable Perl

If this seems useful to people, there are many more articles that could be written and quite a few rough edges that could be smoothed.

Feedback and bug reports are welcome via github

Ovid: Why I wrote Keyword::DEVELOPMENT

I've had a few discussions with people about why I wrote Keyword::DEVELOPMENT, a useful, but simple module. In short, it lets you do this:

use Keyword::DEVELOPMENT;

# later ...

sub consume_item ( $self, $item_slug ) {
    DEVELOPMENT {
      # expensive and time-consuming debugging code here
    }
    my $inventory = $self->inventory;
    return $self->new_exchange(
        slug => 'consume',
        Steps(
            Inventory(  $inventory => contains => $item           ),
            Consumable( $self      => consume  => 'item_instance' ),
            Inventory(  $inventory => remove   => 'item_instance' ),
        ),
    )->attempt;
}

The expensive debug block? With Keyword::DEVELOPMENT, is simply fails to exist in production unless the PERL_KEYWORD_DEVELOPMENT environment variable has a true value. Thus, there is no performance overhead to instrumenting your code with complicated debugging logic (as I'm currently doing with Tau Station).

It's been pointed out to me by several people that I can accomplish the above in regular Perl with constant folding. I'll explain what that means and why I prefer the keyword route.

I prefer this module to constant folding because Keyword::DEVELOPMENT is less fragile (assuming you're comfortable with pluggable keywords: they have been available for about 7 years now) and it creates a standard in our codebase.

With constant folding, you can do the following:

#!/usr/bin/env perl

use 5.024;
use constant DEBUG => $ENV{DEBUG_MY_LOUSY_CODE};

# in exchange...
if (DEBUG) {
    say "Yes, debug!";
}
else {
    say "No, debug!";
}

In the above, if the DEBUG_MY_LOUSY_CODE environment variable doesn't exist, or has a false value, the Perl compiler will see that DEBUG is false, at compile time, and the if block will simply be removed. There won't even be a check for it. It's a powerful tool. Here's the output from perl -MO=Deparse const.pl:

sub BEGIN {
    require 5.024;
}
use constant ('DEBUG', 0);
use strict;
no feature ':all';
use feature ':5.24';
do {
    say 'No, debug!'
};
const.pl syntax OK

Note that the "Yes, debug!" code is not there. That's also what Keyword::DEVELOPMENT does. Except ...

Some dev comes along as says "why are we using the old constant pragma?" So they "fix" the code:

#!/usr/bin/env perl

use 5.024;
use Const::Fast;
const my $DEBUG => $ENV{DEBUG_MY_LOUSY_CODE};

# in exchange...
if ($DEBUG) {
    say "Yes, debug!";
}
else {
    say "No, debug!";
}

And in the deparse:

sub BEGIN {
    require 5.024;
}
use Const::Fast;
use strict;
no feature ':all';
use feature ':5.24';
&const(\my $DEBUG, 0);
if ($DEBUG) {
    say 'Yes, debug!';
}
else {
    say 'No, debug!';
}
const.pl syntax OK

So now we still have our if block there, causing a couple of extra ops which may be problematic in hot code.

And then there's this bit I don't understand at all. In the use constant code, if DEBUG is false, we get a do block instead of the if/else checks. Running perl -MO=Concise,-exec const.pl reveals this:

1  <0> enter
2  <;> nextstate(main 188 const.pl:7) v:*,&,{,$,268437504
3  <0> enter v
4  <;> nextstate(main 192 const.pl:11) v:*,&,$,268437504
5  <0> pushmark s
6  <$> const(PV "No, debug!") s
7  <@> say vK
8  <@> leave vKP
9  <@> leave[1 ref] vKP/REFC

Those are the ops we have with this code and represents what Perl sees internally. Note that this optimized code does not have the "Yes, debug!" code.

Here's the same code, but with DEBUG set to true, thus causing the "Yes, debug!" expression to be printed:

1  <0> enter
2  <;> nextstate(main 188 const.pl:7) v:*,&,{,$,268437504
3  <0> pushmark s
4  <$> const(PV "Yes, debug!") s
5  <@> say vK
6  <@> leave[1 ref] vKP/REFC

We don't get to leverage constant folding, but we actually have fewer ops! If you are running very hot code, shaving ops is important. If you take the else block off, you will get fewer opcodes with the constant folding. I don't actually know why this is, but there you go. I hope some internals guru will come along and tell me what I'm missing here :)

I see constant folding being used all the time for these types of optimizations, with constant names like DEBUG, TESTING, DEBUGGING, and so on. With Keyword::DEVELOPMENT:

  • You get a standard way of handing this
  • Developers thus learn to understand what is happening
  • You have less fragility due to not worrying about how constants are handled

It does require Perl 5 version 12 or above because it uses the pluggable keyword syntax, but it's dead simple:

package Keyword::Development;
use 5.012;   # pluggable keywords
use warnings;
use Keyword::Simple;

sub import {
    Keyword::Simple::define 'DEVELOPMENT', sub {
        my ($ref) = @_;
        if ( $ENV{PERL_KEYWORD_DEVELOPMENT} ) {
            substr( $$ref, 0, 0 ) = 'if (1)';
        }
        else {
            substr( $$ref, 0, 0 ) = 'if (0)';
        }
    };
}

sub unimport {
    Keyword::Simple::undefine 'DEVELOPMENT';
}

Having sane standards in your development shop makes everyone's life easier. Every time you can introduce a single way of accomplishing a task, it's less cognitive overhead for developers and means they spend less time wondering "what does that weird bit do?"

Naturally, you can fork the code from github, download it from the CPAN, or contact our company for training or building awesome software for you. Or just contact me at ovid at allaroundtheworld dot fr.

Note: we write awesome software in a variety of languages, not just Perl.

brian d foy: Nominate Perl heroes for the 2017 White Camel Awards

We're looking for nominations for the 2017 White Camel Awards that recognize significant non-technical achievement in Perl and its community. Each year we recognize work in the broad categories of community, advocacy, and user groups. These people keep the Perl community going and deserve to be recognized!

White baby camel

To nominate someone, you can send me a name and your reasoning through any means you like. Reply here, post on Twitter (note @briandfoy_perl or use the #whitecamelaward tag so I'll find it), send me email, hire landscapers to write out the names in tulips then take an aerial photo you post to instagram, or something more creative. Note, however, that the method of nomination does not factor into our decision!

Sawyer X: Perl 5 Porters Mailing List Summary: November 21st - December 5th

Hey everyone,

Following is the p5p (Perl 5 Porters) mailing list summary for the past two weeks.

Enjoy!

November 21st - December 5th

News and Updates

Our deadline of deprecating $[ is set to 5.30.

ExtUtils::CBuilder updates from 0.280228 to 0.280230.

Grant Reports

  • Zefram 2017 TPF Grant Week 47.

  • Zefram 2017 TPF Grant Week 48.

  • Dave Mitchell TPF Grant 2 weekly report 186.

  • Dave Mitchell TPF Grant 2 weekly report 187.

Issues

New Issues

  • Perl #132481: Minor doc bug in pod/perlop.pod.
  • Perl #132485: Old package separator syntax.
  • Perl #132489: canonical truth values from overloaded predicates.
  • Perl #132490: context consistency for overloaded operators.
  • Perl #132492: Inward goto deprecation has no removal date.
  • Perl #132493: Bleadperl v5.27.5-387-g006c1a1dbd breaks RKINYON/DBM-Deep-2.0014.tar.gz.
  • Perl #132502: Missing warning 'scalar value better written as...' when using reference.
  • Perl #132505: mkdir documentation: "MASK" -> "MODE"?
  • Perl #132506: build failure on NetBSD (likely due to new in-place edit code).
  • Perl #132527: Bleadperl v5.27.5-398-g19a8de4862 breaks MLEHMANN/AnyEvent-HTTP-2.23.tar.gz.
  • Perl #132528: Bleadperl v5.27.5-396-gdd6661605f breaks VPIT/B-RecDeparse-0.10.tar.gz.

Resolved Issues

  • Perl #118139: Storable in DESTROY blocks.
  • Perl #119829: usemymalloc cannot handle long strings.
  • Perl #119831: Data::Dumper: Useqq should apply to glob names, too.
  • Perl #125330: 'Masks earlier declaration' warning move from misc to syntax.
  • Perl #127391: possible inconsistency in "perlop" documentation on associativity of operators.
  • Perl #127712: defined on Hash of Hash defines an element in the hash.
  • Perl #128899: printf %a mishandles several edge cases on long doubles.
  • Perl #130578: op.c: Assertion failure.
  • Perl #131061: Documentation of backslash operator inducing list context for its operand is missing.
  • Perl #131672: perldebguts documentation should be fixed.
  • Perl #131894: runtime error: shift exponent -2 is negative.
  • Perl #132479: t/op/pack.t fails two tests in GCC "long doubles" builds on Windows.
  • Perl #132505: mkdir documentation: "MASK" -> "MODE"?
  • Perl #132527: Bleadperl v5.27.5-398-g19a8de4862 breaks MLEHMANN/AnyEvent-HTTP-2.23.tar.gz.

Suggested Patches

Nicholas R. (Atoomic) provided a patch to add flags for bless and tie to Storable.

Marco Fontani provided a now-merged patch for Perl #132505 (mkdir documentation: "MASK" -> "MODE"?)

Marco also provided a merged patch to document adding patches to RT issues.

Pali provided a patch in Perl #132533 (Devel::PPPort: Implement croak_sv, die_sv, mess_sv, warn_sv and other mess function).

Discussion

Zefram has implemented a revision of smart-match. Discussion ensued.

Zefram also has a proposal for smartmatch signatures.

A discussion was held on Perl #132485 (Old package separator syntax).

There is a conversation happening on the breakage introduced to AnyEvent by fixing a side-effect of an optimization which AnyEvent considers a feature.

Dave's Free Press: Journal: Module pre-requisites analyser

Dave's Free Press: Journal: CPANdeps

Dave's Free Press: Journal: Perl isn't dieing

Dave's Free Press: Journal: YAPC::Europe 2007 report: day 3

Dave's Free Press: Journal: Devel::CheckLib can now check libraries' contents

Ocean of Awareness: Top-down parsing is guessing

Top-down parsing is guessing. Literally. Bottom-up parsing is looking.

The way you'll often hear that phrased is that top-down parsing is looking, starting at the top, and bottom-up parsing is looking, starting at the bottom. But that is misleading, because the input is at the bottom -- at the top there is nothing to look at. A usable top-down parser must have a bottom-up component, even if that component is just lookahead.

A more generous, but still accurate, way to describe the top-down component of parsers is "prediction". And prediction is, indeed, a very useful component of a parser, when used in combination with other techniques.

Of course, if a parser does nothing but predict, it can predict only one input. Top-down parsing must always be combined with a bottom-up component. This bottom-up component may be as modest as lookahead, but it must be there or else top-down parsing is really not parsing at all.

So why is top-down parsing used so much?

Top-down parsing may be unusable in its pure form, but from one point of view that is irrelevant. Top-down parsing's biggest advantage is that it is highly flexible -- there's no reason to stick to its "pure" form.

A top-down parser can be written as a series of subroutine calls -- a technique called recursive descent. Recursive descent allows you to hook in custom-written bottom-up logic at every top-down choice point, and it is a technique which is completely understandable to programmers with little or no training in parsing theory. When dealing with recursive descent parsers, it is more useful to be a seasoned, far-thinking programmer than it is to be a mathematician. This makes recursive descent very appealing to seasoned, far-thinking programmers, and they are the audience that counts.

Switching techniques

You can even use the flexibility of top-down to switch away from top-down parsing. For example, you could claim that a top-down parser could do anything my own parser (Marpa) could do, because a recursive descent parser can call a Marpa parser.

A less dramatic switchoff, and one that still leaves the parser with a good claim to be basically top-down, is very common. Arithmetic expressions are essential for a computer language. But they are also among the many things top-down parsing cannot handle, even with ordinary lookahead. Even so, most computer languages these days are parsed top-down -- by recursive descent. These recursive descent parsers deal with expressions by temporarily handing control over to an bottom-up operator precedence parser. Neither of these parsers is extremely smart about the hand-over and hand-back -- it is up to the programmer to make sure the two play together nicely. But used with caution, this approach works.

Top-down parsing and language-oriented programming

But what about taking top-down methods into the future of language-oriented programming, extensible languages, and grammars which write grammars? Here we are forced to confront the reality -- that the effectiveness of top-down parsing comes entirely from the foreign elements that are added to it. Starting from a basis of top-down parsing is literally starting with nothing. As I have shown in more detail elsewhere, top-down techniques simply do not have enough horsepower to deal with grammar-driven programming.

Perl 6 grammars are top-down -- PEG with lots of extensions. These extensions include backtracking, backtracking control, a new style of tie-breaking and lots of opportunity for the programmer to intervene and customize everything. But behind it all is a top-down parse engine.

One aspect of Perl 6 grammars might be seen as breaking out of the top-down trap. That trick of switching over to a bottom-up operator precedence parser for expressions, which I mentioned above, is built into Perl 6 and semi-automated. (I say semi-automated because making sure the two parsers "play nice" with each other is not automated -- that's still up to the programmer.)

As far as I know, this semi-automation of expression handling is new with Perl 6 grammars, and it may prove handy for duplicating what is done in recursive descent parsers. But it adds no new technique to those already in use. And features like

  • mulitple types of expression, which can be told apart based on their context,
  • n-ary expressions for arbitrary n, and
  • the autogeneration of multiple rules, each allowing a different precedence scheme, for expressions of arbitrary arity and associativity,

all of which are available and in current use in Marpa, are impossible for the technology behind Perl 6 grammars.

I am a fan of the Perl 6 effort. Obviously, I have doubts about one specific set of hopes for Perl 6 grammars. But these hopes have not been central to the Perl 6 effort, and I will be an eager student of the Perl 6 team's work over the coming months.

Comments

To learn more about Marpa, there's the official web site maintained by Ron Savage. I also have a Marpa web site. Comments on this post can be made in Marpa's Google group, or on our IRC channel: #marpa at freenode.net.

Dave's Free Press: Journal: I Love Github

Dave's Free Press: Journal: Palm Treo call db module

Dave's Free Press: Journal: Graphing tool

Dave's Free Press: Journal: Travelling in time: the CP2000AN

Dave's Free Press: Journal: XML::Tiny released

Dave's Free Press: Journal: YAPC::Europe 2007 report: day 1

Ocean of Awareness: Parsing: an expanded timeline

The fourth century BCE: In India, Pannini creates a sophisticated description of the Sanskrit language, exact and complete, and including pronunciation. Sanskrit could be recreated using nothing but Pannini's grammar. Pannini's grammar is probably the first formal system of any kind, predating Euclid. Even today, nothing like it exists for any other natural language of comparable size or corpus. Pannini is the object of serious study today. But in the 1940's and 1950's Pannini is almost unknown in the West. His work has no direct effect on the other events in this timeline.

1943: Emil Post defines and studies a formal rewriting system using productions. With this, the process of reinventing Pannini in the West begins.

1948: Claude Shannon publishes the foundation paper of information theory. Andrey Markov's finite state processes are used heavily.

1952: Grace Hopper writes a linker-loader and describes it as a "compiler". She seems to be the first person to use this term for a computer program. Hopper uses the term "compiler" in its original sense: "something or someone that brings other things together".

1954: At IBM, a team under John Backus begins working on the language which will be called FORTRAN. The term "compiler" is still being used in Hopper's looser sense, instead of its modern one. In particular, there is no implication that the output of a "compiler" is ready for execution by a computer. The output of one 1954 "compiler", for example, produces relative addresses, which need to be translated by hand before a machine can execute them.

1955: Noam Chomsky is awarded a Ph.D. in linguistics and accepts a teaching post at MIT. MIT does not have a linguistics department and Chomsky, in his linguistics course, is free to teach his own approach, highly original and very mathematical.

1956: Chomsky publishes the paper which is usually considered the foundation of Western formal language theory. The paper advocates a natural language approach that involves

  • a bottom layer, using Markov's finite state processes;
  • a middle, syntactic layer, using context-free grammars and context-sensitive grammars; and
  • a top layer, which involves mappings or "transformations" of the output of the syntactic layer.

These layers resemble, and will inspire, the lexical, syntactic and AST transformation phases of modern parsers. For finite state processes, Chomsky acknowledges Markov. The other layers seem to be Chomsky's own formulations -- Chomsky does not cite Post's work.

1957: Steven Kleene discovers regular expressions, a very handy notation for Markov's processes. Regular expressions turn out to describe exactly the mathematical objects being studied as finite state automata, as well as some of the objects being studied as neural nets.

1957: Noam Chomsky publishes Syntactic Structures, one of the most influential books of all time. The orthodoxy in 1957 is structural linguistics which argues, with Sherlock Holmes, that "it is a capital mistake to theorize in advance of the facts". Structuralists start with the utterances in a language, and build upward.

But Chomsky claims that without a theory there are no facts: there is only noise. The Chomskyan approach is to start with a grammar, and use the corpus of the language to check its accuracy. Chomsky's approach will soon come to dominate linguistics.

1957: Backus's team makes the first FORTRAN compiler available to IBM customers. FORTRAN is the first high-level language that will find widespread implementation. As of this writing, it is the oldest language that survives in practical use. FORTRAN is a line-by-line language and its parsing is primitive.

1958: John McCarthy's LISP appears. LISP goes beyond the line-by-line syntax -- it is recursively structured. But the LISP interpreter does not find the recursive structure: the programmer must explicitly indicate the structure herself, using parentheses.

1959: Backus invents a new notation to describe the IAL language (aka ALGOL). Backus's notation is influenced by his study of Post -- he seems not to have read Chomsky until later.

1960: Peter Naur improves the Backus notation and uses it to describe ALGOL 60. The improved notation will become known as Backus-Naur Form (BNF).

1960: The ALGOL 60 report specifies, for the first time, a block structured language. ALGOL 60 is recursively structured but the structure is implicit -- newlines are not semantically significant, and parentheses indicate syntax only in a few specific cases. The ALGOL compiler will have to find the structure. It is a case of 1960's optimism at its best. As the ALGOL committee is well aware, a parsing algorithm capable of handling ALGOL 60 does not yet exist. But the risk they are taking will soon pay off.

1960: A.E. Gleenie publishes his description of a compiler-compiler. Glennie's "universal compiler" is more of a methodology than an implementation -- the compilers must be written by hand. Glennie credits both Chomsky and Backus, and observes that the two notations are "related". He also mentions Post's productions. Glennie may have been the first to use BNF as a description of a procedure instead of as the description of a Chomsky grammar. Glennie points out that the distinction is "important".

Chomskyan BNF and procedural BNF: BNF, when used as a Chomsky grammar, describes a set of strings, and does not describe how to parse strings according to the grammar. BNF notation, if used to describe a procedure, is a set of instructions, to be tried in some order, and used to process a string. Procedural BNF describes a procedure first, and a language only indirectly.

Both procedural and Chomskyan BNF describe languages, but usually not the same language. That is,

  • Suppose D is some BNF description.
  • Let P(D) be D interpreted as a procedure,
  • Let L(P(D)) be the language which the procedure P(D) parses.
  • Let G(D) be D interpreted as a Chomsky grammar.
  • Let L(G(D)) be the language which the grammar G(D) describes.
  • Then, usually, L(P(D)) != L(G(D)).

The pre-Chomskyan approach, using procedural BNF, is far more natural to someone trained as a computer programmer. The parsing problem appears to the programmer in the form of strings to be parsed, exactly the starting point of procedural BNF and pre-Chomsky parsing.

Even when the Chomskyan approach is pointed out, it does not at first seem very attractive. With the pre-Chomskyan approach, the examples of the language more or less naturally lead to a parser. In the Chomskyan approach the programmer has to search for an algorithm to parse strings according to his grammar -- and the search for good algorithms to parse Chomskyan grammars has proved surprisingly long and difficult. Handling semantics is more natural with a Chomksyan approach. But, using captures, semantics can be added to a pre-Chomskyan parser and, with practice, this seems natural enough.

Despite the naturalness of the pre-Chomskyan approach to parsing, we will find that the first fully-described automated parsers are Chomskyan. This is a testimony to Chomsky's influence at the time. We will also see that Chomskyan parsers have been dominant ever since.

1961: In January, Ned Irons publishes a paper describing his ALGOL 60 parser. It is the first paper to fully describe any parser. The Irons algorithm is Chomskyan and top-down with a "left corner" element. The Irons algorithm is general, meaning that it can parse anything written in BNF. It is syntax-driven (aka declarative), meaning that the parser is actually created from the BNF -- the parser does not need to be hand-written.

1961: Peter Lucas publishes the first description of a purely top-down parser. This can be considered to be recursive descent, though in Lucas's paper the algorithm has a syntax-driven implementation, useable only for a restricted class of grammars. Today we think of recursive descent as a methodology for writing parsers by hand. Hand-coded approaches became more popular in the 1960's due to three factors:

  • Memory and CPU were both extremely limited. Hand-coding paid off, even when the gains were small.
  • Non-hand coded top-down parsing, of the kind Lucas's syntax-driven approach allowed, is a very weak parsing technique. It was (and still is) often necessary to go beyond its limits.
  • Top-down parsing is intuitive -- it essentially means calling subroutines. It therefore requires little or no knowledge of parsing theory. This makes it a good fit for hand-coding.

1963: L. Schmidt, Howard Metcalf, and Val Schorre present papers on syntax-directed compilers at a Denver conference.

1964: Schorre publishes a paper on the Meta II "compiler writing language", summarizing the papers of the 1963 conference. Schorre cites both Backus and Chomsky as sources for Meta II's notation. Schorre notes that his parser is "entirely different" from that of Irons 1961 -- in fact it is pre-Chomskyan. Meta II is a template, rather than something that readers can use, but in principle it can be turned into a fully automated compiler-compiler.

1965: Don Knuth invents LR parsing. The LR algorithm is deterministic, Chomskyan and bottom-up, but it is not thought to be practical. Knuth is primarily interested in the mathematics.

1968: Jay Earley invents the algorithm named after him. Like the Irons algorithm, Earley's algorithm is Chomskyan, syntax-driven and fully general. Unlike the Irons algorithm, it does not backtrack. Earley's algorithm is both top-down and bottom-up at once -- it uses dynamic programming and keeps track of the parse in tables. Earley's approach makes a lot of sense and looks very promising indeed, but there are three serious issues:

  • First, there is a bug in the handling of zero-length rules.
  • Second, it is quadratic for right recursions.
  • Third, the bookkeeping required to set up the tables is, by the standards of 1968 hardware, daunting.

1969: Frank DeRemer describes a new variant of Knuth's LR parsing. DeRemer's LALR algorithm requires only a stack and a state table of quite manageable size. LALR looks practical.

1969: Ken Thompson writes the "ed" editor as one of the first components of UNIX. At this point, regular expressions are an esoteric mathematical formalism. Through the "ed" editor and its descendants, regular expressions will become an everyday part of the working programmer's toolkit.

Recognizers: In comparing algorithms, it can be important to keep in mind whether they are recognizers or parsers. A recognizer is a program which takes a string and produces a "yes" or "no" according to whether a string is in part of a language. Regular expressions are typically used as recognizers. A parser is a program which takes a string and produces a tree reflecting its structure according to a grammar. The algorithm for a compiler clearly must be a parser, not a recognizer. Recognizers can be, to some extent, used as parsers by introducing captures.

1972: Alfred Aho and Jeffrey Ullman publish a two volume textbook summarizing the theory of parsing. This book is still important. It is also distressingly up-to-date -- progress in parsing theory slowed dramatically after 1972. Aho and Ullman describe a straightforward fix to the zero-length rule bug in Earley's original algorithm. Unfortunately, this fix involves adding even more bookkeeping to Earley's.

1972: Under the names TDPL and GTDPL, Aho and Ullman investigate the non-Chomksyan parsers in the Schorre lineage. They note that "it can be quite difficult to determine what language is defined by a TDPL parser". That is, GTDPL parsers do whatever they do, and that whatever is something the programmer in general will not be able to describe. The best a programmer can usually do is to create a test suite and fiddle with the GTDPL description until it passes. Correctness cannot be established in any stronger sense. GTDPL is an extreme form of the old joke that "the code is the documentation" -- with GTDPL nothing documents the language of the parser, not even the code.

GTDPL's obscurity buys nothing in the way of additional parsing power. Like all non-Chomskyan parsers, GTDPL is basically a extremely powerful recognizer. Pressed into service as a parser, it is comparatively weak. As a parser, GTDPL is essentially equivalent to Lucas's 1961 syntax-driven algorithm, which was in turn a restricted form of recursive descent.

At or around this time, rumor has it that the main line of development for GTDPL parsers is classified secret by the US government. GTDPL parsers have the property that even small changes in GTDPL parsers can be very labor-intensive. For some government contractors, GTDPL parsing provides steady work for years to come. Public interest in GTDPL fades.

1975: Bell Labs converts its C compiler from hand-written recursive descent to DeRemer's LALR algorithm.

1977: The first "Dragon book" comes out. This soon-to-be classic textbook is nicknamed after the drawing on the front cover, in which a knight takes on a dragon. Emblazoned on the knight's lance are the letters "LALR". From here on out, to speak lightly of LALR will be to besmirch the escutcheon of parsing theory.

1979: Bell Laboratories releases Version 7 UNIX. V7 includes what is, by far, the most comprehensive, useable and easily available compiler writing toolkit yet developed.

1979: Part of the V7 toolkit is Yet Another Compiler Compiler (YACC). YACC is LALR-powered. Despite its name, YACC is the first compiler-compiler in the modern sense. For some useful languages, the process of going from Chomskyan specification to executable is fully automated. Most practical languages, including the C language and YACC's own input language, still require manual hackery. Nonetheless, after two decades of research, it seems that the parsing problem is solved.

1987: Larry Wall introduces Perl 1. Perl embraces complexity like no previous language. Larry uses YACC and LALR very aggressively -- to my knowledge more aggressively than anyone before or since.

1991: Joop Leo discovers a way of speeding up right recursions in Earley's algorithm. Leo's algorithm is linear for just about every unambiguous grammar of practical interest, and many ambiguous ones as well. In 1991 hardware is six orders of magnitude faster than 1968 hardware, so that the issue of bookkeeping overhead had receded in importance. This is a major discovery. When it comes to speed, the game has changed in favor of the Earley algorithm.

But Earley parsing is almost forgotten. Twenty years will pass before anyone writes a practical implementation of Leo's algorithm.

1990's: Earley's is forgotten. So everyone in LALR-land is content, right? Wrong. Far from it, in fact. Users of LALR are making unpleasant discoveries. While LALR automatically generates their parsers, debugging them is so hard they could just as easily write the parser by hand. Once debugged, their LALR parsers are fast for correct inputs. But almost all they tell the users about incorrect inputs is that they are incorrect. In Larry's words, LALR is "fast but stupid".

2000: Larry Wall decides on a radical reimplementation of Perl -- Perl 6. Larry does not even consider using LALR again.

2002: John Aycock and R. Nigel Horspool publish their attempt at a fast, practical Earley's parser. Missing from it is Joop Leo's improvement -- they seem not to be aware of it. Their own speedup is limited in what it achieves and the complications it introduces can be counter-productive at evaluation time. But buried in their paper is a solution to the zero-length rule bug. And this time the solution requires no additional bookkeeping.

2004: Bryan Ford publishes his paper on PEG. Implementers by now are avoiding YACC, and it seems as if there might soon be no syntax-driven algorithms in practical use. Ford fills this gap by repackaging the nearly-forgotten GTDPL. Ford adds packratting, so that PEG is always linear, and provides PEG with an attractive new syntax. But nothing has been done to change the problematic behaviors of GTDPL.

2006: GNU announces that the GCC compiler's parser has been rewritten. For three decades, the industry's flagship C compilers have used LALR as their parser -- proof of the claim that LALR and serious parsing are equivalent. Now, GNU replaces LALR with the technology that it replaced a quarter century earlier: recursive descent.

Today: After five decades of parsing theory, the state of the art seems to be back where it started. We can imagine someone taking Ned Iron's original 1961 algorithm from the first paper ever published describing a parser, and republishing it today. True, he would have to translate its code from the mix of assembler and ALGOL into something more fashionable, say Haskell. But with that change, it might look like a breath of fresh air.

Marpa: an afterword

The recollections of my teachers cover most of this timeline. My own begin around 1970. Very early on, as a graduate student, I became unhappy with the way the field was developing. Earley's algorithm looked interesting, and it was something I returned to on and off.

The original vision of the 1960's was a parser that was

  • efficient,
  • practical,
  • general, and
  • syntax-driven.

By 2010 this vision seemed to have gone the same way as many other 1960's dreams. The rhetoric stayed upbeat, but parsing practice had become a series of increasingly desperate compromises.

But, while nobody was looking for them, the solutions to the problems encountered in the 1960's had appeared in the literature. Aycock and Horspool had solved the zero-length rule bug. Joop Leo had found the speedup for right recursion. And the issue of bookkeeping overhead had pretty much evaporated on its own. Machine operations are now a billion times faster than in 1968, and are probably no longer relevant in any case -- cache misses are now the bottleneck.

The programmers of the 1960's would have been prepared to trust a fully declarative Chomskyan parser. With the experience with LALR in their collective consciousness, modern programmers might be more guarded. As Lincoln said, "Once a cat's been burned, he won't even sit on a cold stove." But I found it straightforward to rearrange the Earley parse engine to allow efficient event-driven handovers between procedural and syntax-driven logic. And Earley tables provide the procedural logic with full knowledge of the state of the parse so far, so that Earley's algorithm is a better platform for hand-written procedural logic than recursive descent.

References, comments, etc.

My implementation of Earley's algorithm is called Marpa. For more about Marpa, there is the semi-official web site, maintained by Ron Savage. The official, but more limited, Marpa website is my personal one. Comments on this post can be made in Marpa's Google group, or on our IRC channel: #marpa at freenode.net.

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Ocean of Awareness: What are the reasonable computer languages?

"You see things; and you say 'Why?' But I dream things that never were; and I say 'Why not?'" -- George Bernard Shaw

In the 1960's and 1970's computer languages were evolving rapidly. It was not clear which way they were headed. Would most programming be done with general-purpose languages? Or would programmers create a language for every task domain? Or even for every project? And, if lots of languages were going to be created, what kinds of languages would be needed?

It was in that context that Čulik and Cohen, in a 1973 paper, outlined what they thought programmers would want and should have. In keeping with the spirit of the time, it was quite a lot:

  • Programmers would want to extend their grammars with new syntax, including new kinds of expressions.
  • Programmers would also want to use tools that automatically generated new syntax.
  • Programmers would not want to, and especially in the case of auto-generated syntax would usually not be able to, massage the syntax into very restricted forms. Instead, programmers would create grammars and languages which required unlimited lookahead to disambiguate, and they would require parsers which could handle these grammars.
  • Finally, programmers would need to be able to rely on all of this parsing being done in linear time.

Today, we think we know that Čulik and Cohen's vision was naive, because we think we know that parsing technology cannot support it. We think we know that parsing is much harder than they thought.

The eyeball grammars

As a thought problem, consider the "eyeball" class of grammars. The "eyeball" class of grammars contains all the grammars that a human can parse at a glance. If a grammar is in the eyeball class, but a computer cannot parse it, it presents an interesting choice. Either,

  • your computer is not using the strongest practical algorithm; or
  • your mind is using some power which cannot be reduced to a machine computation.

There are some people out there (I am one of them) who don't believe that everything the mind can do reduces to a machine computation. But even those people will tend to go for the choice in this case: There must be some practical computer parsing algorithm which can do at least as well at parsing as a human can do by "eyeball". In other words, the class of "reasonable grammars" should contain the eyeball class.

Čulik and Cohen's candidate for the class of "reasonable grammars" were the grammars that a deterministic parse engine could parse if it had a lookahead that was infinite, but restricted to distinguishing between regular expressions. They called these the LR-regular, or LRR, grammars. And the LRR grammars do in fact seem to be a good first approximation to the eyeball class. They do not allow lookahead that contains things that you have to count, like palindromes. And, while I'd be hard put to eyeball every possible string for every possible regular expression, intuitively the concept of scanning for a regular expression does seem close to capturing the idea of glancing through a text looking for a telltale pattern.

So what happened?

Alas, the algorithm in the Čulik and Cohen paper turned out to be impractical. But in 1991, Joop Leo discovered a way to adopt Earley's algorithm to parse the LRR grammars in linear time, without doing the lookahead. And Leo's algorithm does have a practical implementation: Marpa.

References, comments, etc.

To learn more about Marpa, there's the official web site maintained by Ron Savage. I also have a Marpa web site. Comments on this post can be made in Marpa's Google group, or on our IRC channel: #marpa at freenode.net.

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Ocean of Awareness: What parser do birds use?

"Here we provide, to our knowledge, the first unambiguous experimental evidence for compositional syntax in a non-human vocal system." -- "Experimental evidence for compositional syntax in bird calls", Toshitaka N. Suzuki, David Wheatcroft & Michael Griesser Nature Communications 7, Article number: 10986

In this post I look at a subset of the language of the Japanese great tit, also known as Parus major. The above cited article presents evidence that bird brains can parse this language. What about standard modern computer parsing methods? Here is the subset -- probably a tiny one -- of the language actually used by Parus major.

      S ::= ABC
      S ::= D
      S ::= ABC D
      S ::= D ABC
    

Classifying the Parus major grammar

Grammophone is a very handy new tool for classifying grammars. Its own parser is somewhat limited, so that it requires a period to mark the end of a rule. The above grammar is in Marpa's SLIF format, which is smart enough to use the "::=" operator to spot the beginning and end of rules, just as the human eye does. Here's the same grammar converted into a form acceptable to Grammophone:

      S -> ABC .
      S -> D .
      S -> ABC D .
      S -> D ABC .
    

Grammophone tells us that the Parus major grammar is not LL(1), but that it is LALR(1).

What does this mean?

LL(1) is the class of grammar parseable by top-down methods: it's the best class for characterizing most parsers in current use, including recursive descent, PEG, and Perl 6 grammars. All of these parsers fall short of dealing with the Parus major language.

LALR(1) is probably most well-known from its implementations in bison and yacc. While able to handle this subset of Parus's language, LALR(1) has its own, very strict, limits. Whether LALR(1) could handle the full complexity of Parus language is a serious question. But it's a question that in practice would probably not arise. LALR(1) has horrible error handling properties.

When the input is correct and within its limits, an LALR-driven parser is fast and works well. But if the input is not perfectly correct, LALR parsers produce no useful analysis of what went wrong. If Parus hears "d abc d", a parser like Marpa, on the other hand, can produce something like this:

# * String before error: abc d\s
# * The error was at line 1, column 7, and at character 0x0064 'd', ...
# * here: d
    

Parus uses its language in predatory contexts, and one can assume that a Parus with a preference for parsers whose error handling is on an LALR(1) level will not be keeping its alleles in the gene pool for very long.

References, comments, etc.

Those readers content with sub-Parus parsing methods may stop reading here. Those with greater parsing ambitions, however, may wish to learn more about Marpa. A Marpa test script for parsing the Parus subset is in a Github gist. Marpa has a semi-official web site, maintained by Ron Savage. The official, but more limited, Marpa website is my personal one. Comments on this post can be made in Marpa's Google group, or on our IRC channel: #marpa at freenode.net.

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Ocean of Awareness: Introduction to Marpa Book in progress

What follows is a summary of the features of the Marpa algorithm, followed by a discussion of potential applications. It refers to itself as a "monograph", because it is a draft of part of the introduction to a technical monograph on the Marpa algorithm. I hope the entire monograph will appear in a few weeks.

The Marpa project

The Marpa project was intended to create a practical and highly available tool to generate and use general context-free parsers. Tools of this kind had long existed for LALR and regular expressions. But, despite an encouraging academic literature, no such tool had existed for context-free parsing. The first stable version of Marpa was uploaded to a public archive on Solstice Day 2011. This monograph describes the algorithm used in the most recent version of Marpa, Marpa::R2. It is a simplification of the algorithm presented in my earlier paper.

A proven algorithm

While the presentation in this monograph is theoretical, the approach is practical. The Marpa::R2 implementation has been widely available for some time, and has seen considerable use, including in production environments. Many of the ideas in the parsing literature satisfy theoretical criteria, but in practice turn out to face significant obstacles. An algorithm may be as fast as reported, but may turn out not to allow adequate error reporting. Or a modification may speed up the recognizer, but require additional processing at evaluation time, leaving no advantage to compensate for the additional complexity.

In this monograph, I describe the Marpa algorithm as it was implemented for Marpa::R2. In many cases, I believe there are better approaches than those I have described. But I treat these techniques, however solid their theory, as conjectures. Whenever I mention a technique that was not actually implemented in Marpa::R2, I will always explicitly state that that technique is not in Marpa as implemented.

Features

General context-free parsing

As implemented, Marpa parses all "proper" context-free grammars. The proper context-free grammars are those which are free of cycles, unproductive symbols, and inaccessible symbols. Worst case time bounds are never worse than those of Earley's algorithm, and therefore never worse than O(n**3).

Linear time for practical grammars

Currently, the grammars suitable for practical use are thought to be a subset of the deterministic context-free grammars. Using a technique discovered by Joop Leo, Marpa parses all of these in linear time. Leo's modification of Earley's algorithm is O(n) for LR-regular grammars. Leo's modification also parses many ambiguous grammars in linear time.

Left-eidetic

The original Earley algorithm kept full information about the parse --- including partial and fully recognized rule instances --- in its tables. At every parse location, before any symbols are scanned, Marpa's parse engine makes available its information about the state of the parse so far. This information is in useful form, and can be accessed efficiently.

Recoverable from read errors

When Marpa reads a token which it cannot accept, the error is fully recoverable. An application can try to read another token. The application can do this repeatedly as long as none of the tokens are accepted. Once the application provides a token that is accepted by the parser, parsing will continue as if the unsuccessful read attempts had never been made.

Ambiguous tokens

Marpa allows ambiguous tokens. These are often useful in natural language processing where, for example, the same word might be a verb or a noun. Use of ambiguous tokens can be combined with recovery from rejected tokens so that, for example, an application could react to the rejection of a token by reading two others.

Using the features

Error reporting

An obvious application of left-eideticism is error reporting. Marpa's abilities in this respect are ground-breaking. For example, users typically regard an ambiguity as an error in the grammar. Marpa, as currently implemented, can detect an ambiguity and report specifically where it occurred and what the alternatives were.

Event driven parsing

As implemented, Marpa::R2 allows the user to define "events". Events can be defined that trigger when a specified rule is complete, when a specified rule is predicted, when a specified symbol is nulled, when a user-specified lexeme has been scanned, or when a user-specified lexeme is about to be scanned. A mid-rule event can be defined by adding a nulling symbol at the desired point in the rule, and defining an event which triggers when the symbol is nulled.

Ruby slippers parsing

Left-eideticism, efficient error recovery, and the event mechanism can be combined to allow the application to change the input in response to feedback from the parser. In traditional parser practice, error detection is an act of desperation. In contrast, Marpa's error detection is so painless that it can be used as the foundation of new parsing techniques.

For example, if a token is rejected, the lexer is free to create a new token in the light of the parser's expectations. This approach can be seen as making the parser's "wishes" come true, and I have called it "Ruby Slippers Parsing".

One use of the Ruby Slippers technique is to parse with a clean but oversimplified grammar, programming the lexical analyzer to make up for the grammar's short-comings on the fly. As part of Marpa::R2, the author has implemented an HTML parser, based on a grammar that assumes that all start and end tags are present. Such an HTML grammar is too simple even to describe perfectly standard-conformant HTML, but the lexical analyzer is programmed to supply start and end tags as requested by the parser. The result is a simple and cleanly designed parser that parses very liberal HTML and accepts all input files, in the worst case treating them as highly defective HTML.

Ambiguity as a language design technique

In current practice, ambiguity is avoided in language design. This is very different from the practice in the languages humans choose when communicating with each other. Human languages exploit ambiguity in order to design highly flexible, powerfully expressive languages. For example, the language of this monograph, English, is notoriously ambiguous.

Ambiguity of course can present a problem. A sentence in an ambiguous language may have undesired meanings. But note that this is not a reason to ban potential ambiguity --- it is only a problem with actual ambiguity.

Syntax errors, for example, are undesired, but nobody tries to design languages to make syntax errors impossible. A language in which every input was well-formed and meaningful would be cumbersome and even dangerous: all typos in such a language would be meaningful, and parser would never warn the user about errors, because there would be no such thing.

With Marpa, ambiguity can be dealt with in the same way that syntax errors are dealt with in current practice. The language can be designed to be ambiguous, but any actual ambiguity can be detected and reported at parse time. This exploits Marpa's ability to report exactly where and what the ambiguity is. Marpa::R2's own parser description language, the SLIF, uses ambiguity in this way.

Auto-generated languages

In 1973, Čulik and Cohen pointed out that the ability to efficiently parse LR-regular languages opens the way to auto-generated languages. In particular, Čulik and Cohen note that a parser which can parse any LR-regular language will be able to parse a language generated using syntax macros.

Second order languages

In the literature, the term "second order language" is usually used to describe languages with features which are useful for second-order programming. True second-order languages --- languages which are auto-generated from other languages --- have not been seen as practical, since there was no guarantee that the auto-generated language could be efficiently parsed.

With Marpa, this barrier is raised. As an example, Marpa::R2's own parser description language, the SLIF, allows "precedenced rules". Precedenced rules are specified in an extended BNF. The BNF extensions allow precedence and associativity to be specified for each RHS.

Marpa::R2's precedenced rules are implemented as a true second order language. The SLIF representation of the precedenced rule is parsed to create a BNF grammar which is equivalent, and which has the desired precedence. Essentially, the SLIF does a standard textbook transformation. The transformation starts with a set of rules, each of which has a precedence and an associativity specified. The result of the transformation is a set of rules in pure BNF. The SLIF's advantage is that it is powered by Marpa, and therefore the SLIF can be certain that the grammar that it auto-generates will parse in linear time.

Notationally, Marpa's precedenced rules are an improvement over similar features in LALR-based parser generators like yacc or bison. In the SLIF, there are two important differences. First, in the SLIF's precedenced rules, precedence is generalized, so that it does not depend on the operators: there is no need to identify operators, much less class them as binary, unary, etc. This more powerful and flexible precedence notation allows the definition of multiple ternary operators, and multiple operators with arity above three.

Second, and more important, a SLIF user is guaranteed to get exactly the language that the precedenced rule specifies. The user of the yacc equivalent must hope their syntax falls within the limits of LALR.

References, comments, etc.

Marpa has a semi-official web site, maintained by Ron Savage. The official, but more limited, Marpa website is my personal one. Comments on this post can be made in Marpa's Google group, or on our IRC channel: #marpa at freenode.net.

Header image by Tambako the Jaguar. Some rights reserved.