I need to write a custom parser for EXPRESS - which is mentioned to be a data modeling language that is used to define and pass construction information around for CAD software.
Here are couple resources:
https://en.wikipedia.org/wiki/EXPRESS_(data_modeling_language)
https://www.loc.gov/preservation/digital/formats/fdd/fdd000449.shtml
Well, I need to come up with a custom parser for this "data modeling language" . However, I have no idea what specifics I need to pay attention to before I can start implementing a decent parser. In what ways should I analyze this text-based format before deciding how to parse it and represent it in a meaningful way?
What do I specifically need to know about this "data modeling language" and its syntax so that I can come up with a reasonable parser?
There are descriptions of the EXPRESS language in Backus-Naur-Form on github. There are tools that take a description in BNF and generate a parser from it (for example bison or boost::spirit).
These will give you a working text parser for the language. The next step is to give the parsed text a meaning. EXPRESS usually describes a class hierarchy and certain constraints, so you will need to model that with the tokens you get from the parser.
You might want to take a look at existing implementations, for example stepcode. They have an EXPRESS parser which takes the EXPRESS schema and generates a STEP parser which can load files described by the EXPRESS schema.
You should know that EXPRESS and STEP are very powerful and extensive tools, so you should consider using/modifying existing implementations instead of rolling your own.
There is a parser based on xtext in the EXPRESS2EMF project. It is not yet complete though. Currently it is still skipping constraints (aka where rules) and some EXPRESS language constructs that are not used in IFC specifications.
Here you can find a parser for C++ implemented using Flex and Bison: oipExpress
Related
I'm looking into the feasibility of writing a code formatting tool for the Apex language, a Salesforce.com variation on Java, and perhams VisualForce, its tag based markup language.
I have no idea on where to start this, apart from feeling/knowing that writing a language parser from scratch is probably not the best approach.
I have a fairly thin grasp of what Antlr is and what it does, but conceptually, I'm imagining one could 'train' antlr to understand the syntax of Apex. I could then get a structured version of the code in a data structure (AST?) which I could then walk to produce correctly formatted code.
Is this the right concept? Is Antlr a tool to do that? Any links to a brief synopsis on this? I'm looking for investing a few days in this task, not months, and I'm not sure if its even vaguely achievable.
Since Apex syntax is similar to Java, I'd look at Eclipse's JDT. Edit down the Java grammar to match Apex. Do the same w/ formatting rules/options. This is more than a few days of work.
Steven Herod wrote:
... I'm imagining one could 'train' antlr to understand the syntax of Apex. ...
What do you mean by "'train' antlr"? "Train" as in artificial intelligence (training a neural-net)? If so, then you are mistaken.
Steven Herod wrote:
... get a structured version of the code in a data structure (AST?) which I could then walk to produce correctly formatted code.
Is this the right concept? Is Antlr a tool to do that?
Yes, more or less. You write a grammar that precisely defines the language you want to parse. Then you use ANTLR which will generate a lexer (tokenizer) and parser based on the grammar file. You can let the parser create an AST from your input source and then walk the AST and emit (custom) output/code.
Steven Herod wrote:
... I'm looking for investing a few days in this task, not months, and I'm not sure if its even vaguely achievable.
Well, I don't know you of course, but I'd say writing a grammar for a language similar to Java, and then emitting output by walking the AST within just a couple of days is impossible, even more so for someone new to ANTLR. I am fairly familiar with ANTLR, but I couldn't do it in just a few days. Note that I'm only talking about the "parsing-part", after you've done that, you'll need to integrate this in some text editor. This all looks to be more a project of several months, not even weeks, let alone several days.
So, in short, if all you want to do is write a custom code highlighter, ANTLR isn't your best choice.
You could have a look at Xtext which uses ANTLR under the hood. To quote their website:
With Xtext you can easily create your own programming languages and domain-specific languages (DSLs). The framework supports the development of language infrastructures including compilers and interpreters as well as full blown Eclipse-based IDE integration. ...
But I doubt you'll have an Eclipse plugin up and running within just a few days.
Anyway, best of luck!
Our DMS Software Reengineering Toolkit is designed to do this as kind poker-pot ante necessary to do any kind of automated software reengineering project.
DMS allows one to define a grammar, similar to ANTLR's (and other parser generator) styles. Unlike ANTLR (and other parser generators), DMS uses a GLR parser, which means you don't have to bend the language grammar rules to meet the requirements of the parser generator. If you can write an context-free grammar, DMS will convert that into a parser for that language. This means in fact you can get a working, correct grammar up considerably faster than with typical LL or L(AL)R parser generators.
Unlike ANTLR (and other parser generators), there is no additional work to build the AST; it is automatically constructed. This means you spend zero time write tree-building rules and none debugging them.
DMS additionally provides a pretty-printing specification language, specifying text boxes stack vertically, horizontally, or indented, in which you can define the "format" that is used to convert the AST back into completely legal, nicely formatted source text. None of the well known parser generators provide any help here; if you want to prettyprint the tree, you get to do a great deal of custom coding. For more details on this, see my SO answer to Compiling an AST back to source. What this means is you can build a prettyprinter for your grammar in an (intense) afternoon by simply annotating the grammar rules with box layout directives.
DMS's lexer is very careful to capture comments and "lexical formats" (was that number octal? What kind of quotes did that string have? Escaped characters?) so that they can be regenerated correctly. Parse-to-AST and then prettyprint-AST-to-text round trips arbitrarily ugly code into formatted code following the prettyprinting rules. (This round trip is the poker ante: if you want go further, to actually manipulate the AST, you still want to be able to regenerate valid source text).
We recently built parser/prettyprinters for EGL. This took about a week end to end. Granted, we are expert at our tools.
You can download any of a number of different formatters built using DMS from our web site, to see what such formatting can do.
EDIT July 2012: Last week (5 days) using DMS, from scratch we (I personally) built a fully compliant IEC61131-3 "Structured Text" (industrial control language, Pascal-like) parser and prettyprinter. (It handles all the examples from the standards documents).
Reverse engineering a language to get a parser is hard. Very hard! Even if it's very close to Java.
But why reinvent the wheel?
There is a wonderful Apex parser implementation as part of the Force.com IDE on GitHub. It's just a jar without source code but you can use it for whatever you want. And the developers behind it are really supportive and helpful.
We are currently building an Apex module of the famous Java static code analyzer PMD here. And we use Salesforce.com internal parser. It works like a charm.
And hey, it's an open source project and we need contributers of any kind ;-)
I'm currently developing a general-purpose agent-based programming language (its syntaxt will be somewhat inspired by Java, and we are also using object in this language).
Since the beginning of the project we were doubtful about the fact of using ANTLR or Xtext. At that time we found out that Xtext was implementing a subset of the feature of ANTLR. So we decided to use ANLTR for our language losing the possibility to have a full-fledged Eclipse editor for free for our language (such a nice features provided by Xtext).
However, as the best of my knowledge, this summer the Xtext project has done a big step forward. Quoting from the link:
What are the limitations of Xtext?
Sven: You can implement almost any kind of programming language or DSL
with Xtext. There is one exception, that is if you need to use so
called 'Semantic Predicates' which is a rather complicated thing I
don't think is worth being explained here. Very few languages really
need this concept. However the prominent example is C/C++. We want to
look into that topic for the next release.
And that is also reinforced in the Xtext documentation:
What is Xtext? No matter if you want to create a small textual domain-specific language (DSL) or you want to implement a full-blown
general purpose programming language. With Xtext you can create your
very own languages in a snap. Also if you already have an existing
language but it lacks decent tool support, you can use Xtext to create
a sophisticated Eclipse-based development environment providing
editing experience known from modern Java IDEs in a surprisingly short
amount of time. We call Xtext a language development framework.
If Xtext has got rid of its past limitations why is it still not possible to find a complex Xtext grammar for the best known programming languages (Java, C#, etc.)?
On the ANTLR website you can find tons of such grammar examples, for what concerns Xtext instead the only sample I was able to find is the one reported in the documentation. So maybe Xtext is still not mature to be used for implementing a general purpose programming language? I'm a bit worried about this... I would not start to re-write the grammar in Xtext for then to recognize that it was not suited for that.
I think nobody implemented Java or C++ because it is a lot of work (even with Xtext) and the existing tools and compilers are excellent.
However, you could have a look at Xbase and Xtend, which is the expression language we ship with Xtext. It is built with Xtext and is quite a good proof for what you can build with Xtext. We have done that in about 4 person months.
I did a couple of screencasts on Xtend:
http://blog.efftinge.de/2011/03/xtend-screencast-part-1-basics.html
http://blog.efftinge.de/2011/03/xtend-screencast-part-2-switch.html
http://blog.efftinge.de/2011/03/xtend-screencast-part-3-rich-strings-ie.html
Note, that you can simply embed Xbase expressions into your language.
I can't speak for what Xtext is or does well.
I can speak to the problem of developing robust tools for processing real languages, based on our experience with the DMS Software Reengineering Toolkit, which we imagine is a language manipulation framework.
First, parsing of real languages usually involves something messy in lexing and/or parsing, due to the historical ways these languages have evolved. Java is pretty clean. C# has context-dependent keywords and a rudimentary preprocessor sort of like C's. C has a full blown preprocessor. C++ is famously "hard to parse" due to ambiguities in the grammar and shenanigans with template syntax. COBOL is fairly ugly, doesn't have any reference grammars, and comes in a variety of dialects. PHP will turn you to stone if you look at it because it is so poorly defined. (DMS has parsers for all of these, used in anger on real applications).
Yet you can parse all of these with most of the available parsing technologies if you try hard enough, usually by abusing the lexer or the parser to achieve your goals (how the GNU guys abused Bison to parse C++ by tangling lexical analysis with symbol table lookup is a nice ugly case in point). But it takes a lot of effort to get the language details right, and the reference manuals are only close approximations of the truth with respect to what the compilers really accept.
If Xtext has a decent parsing engine, one can likely do this with Xtext. A brief perusal of the Xtext site sounds like the lexers and parsers are fairly decent. I didn't see anything about the "Semantic Predicate"s; we have them in DMS and they are lifesavers in some of the really dark corners of parsing. Even using the really good parsing technology (we use GLR parsers), it would be very hard to parse COBOL data declarations (extracting their nesting structure during the parse) without them.
You have an interesting problem in that your language isn't well defined yet. That will make your initial parsers somewhat messy, and you'll revise them a lot. Here's where strong parsing technology helps you: if you can revise your grammar easily you can focus on what you want your language to look like, rather than focusing on fighting the lexer and parser. The fact that you can change your language definition means in fact that if Xtext has some limitations, you can probably bend your language syntax to match without huge amounts of pain. ANTLR does have the proven ability to parse a language pretty much as you imagine it, modulo the usual amount of parser hacking.
What is never discussed is what else is needed to process a language for real. The first thing you need to be able to do is to construct ASTs, which ANTLR and YACC will help you do; I presume Xtext does also. You also need symbol tables, control and data flow analysis (both local and global), and machinery to transform your language into something else (presumably more executable). Doing just symbol tables you will find surprisingly hard; C++ has several hundred pages of "how to look up an identifier"; Java generics are a lot tougher to get right than you might expect. You might also want to prettyprint the AST back to source code, if you want to offer refactorings. (EDIT: Here both ANTLR and Xtext offer what amounts to text-template driven code generation).
Yet these are complex mechanisms that take as much time, if not more than building the parser. The reason DMS exists isn't because it can parse (we view this just as the ante in a poker game), but because all of this other stuff is very hard and we wanted to amortize the cost of doing it all (DMS has, we think, excellent support for all of these mechanisms but YMMV).
On reading the Xtext overview, it sounds like they have some support for symbol tables but it is unclear what kind of assumption is behind it (e.g., for C++ you have to support multiple inheritance and namespaces).
If you are already started down the ANTLR road and have something running, I'd be tempted to stay the course; I doubt if Xtext will offer you a lot of additional help. If you really really want Xtext's editor, then you can probably switch at the price of restructuring what grammar you have (this is a pretty typical price to pay when changing parsing paradigms). Expect most of your work to appear after you get the parser right, in an ad hoc way. I doubt you will find Xtext or ANTLR much different here.
I guess the most simple answer to your question is: Many general purpose languages can be implemented using Xtext. But since there is no general answer to which parser-capabilities a general purpose languages needs, there is no general answer to your questions.
However, I've got a few pointers:
With Xtext 2.0 (released this summer), Xtext supports syntactic predicates. This is one of the most requested features to handle ambiguous syntax without enabling antlr's backtracking.
You might want to look at the brand-new languages Xbase and Xtend, which are (judging based on their capabilities) general-purpose and which are developed using Xtext. Sven has some nice screen casts in his blog: http://blog.efftinge.de/
Regarding your question why we don't see Xtext-grammars for Java, C++, etc.:
With Xtext, a language is more than just a grammar, so just having a grammar that describes a language's syntax is a good starting point but usually not an artifact valuable enough for shipping. The reason is that with an Xtext-grammar you also define the AST's structure (Abstract Syntax Tree, and an Ecore Model in fact) including true cross references. Since this model is the main internal API of your language people usually spend a lot of thought designing it. Furthermore, to resolve cross references (aka linking) you need to implement scoping (as it is called in Xtext). Without a proper implementation of scoping you can either not have true cross references in your model or you'll get many lining errors.
A guess my point is that creating a grammar + designing the AST model + implementing scoping is just a little more effort that taking a grammar from some language-zoo and translating it to Xtext's syntax.
I need to parse C#, Ruby and Python source code to generate some reports. I need to get a list of method names inside a class, and I need some other info such as usage of global variable or something. Just parsing using RE could be a solution, but I expect a better (systematic) solution using parsers, if it is easily possible.
What parsers for those languages are provided?
For C#, I found http://csparser.codeplex.com/Wikipage , but for the others, I found a bunch of parsers using those languages, but not the language parsers of them.
It may be worth looking into the ANTLR parser generator.
You'll find, on the ANTLR site, grammars for all 3 languages you are interested in (Although the Ruby grammar is only for a "simplified" version of the language).
The next difficulty may be to adapt these grammars for the particular target language you would like, i.e. the language in which the parsers themselves will be generated. ANTLR's grammar language is very expressive, allowing one to deal with various context-sensitive languages. This is done by inserting various snippets (in the target language) and/or semantic or syntactic predicates (also in the target language) amid the EBNF-like grammar; consequently the grammar is a bit messier and may need adapting when the target language is changed. The "native" target language of ANTLR is Java, but many other targets languages are supported.
On the whole, ANTLR represents a bit a setup/learning-curve effort, but since you need to deal with 3 languages, it may well be worth the investment, as this will allow you to have a uniform framework (over which you have "full" control), rather than trying to corral three possibly very distinct, and possibly more "locked down" parsers as you started doing.
All three languages are relatively sophisticated languages and although your goal is "merely" to identify methods within programs, you may be able to hack/simplify some of the grammars (or maybe simply "ignore" parts of them), only mapping the few parser-level rules of interest to your eventual goal.
Once these rules are identified, you can apply the same or similar actions, i.e. snippets (in the target language) which implement what you wish to accomplish when the parser encounters such rules (eg: store the method's signature for future reporting, start counting the number of lines... whatever).
A final suggestion:
As hinted in comments to the question, and depending on your goals, you may be able to reuse existing utility programs to perform directly, or indirectly these goals.
Also, because indeed messing with parsers for these sophisticated languages may be somewhat overkill for you possibly simple and possibly error-tolerant goals, the Regular Expressions approach may fit the bill, somehow; the fact of the matter is that none of these languages are regular nor context free, so success with regex will be highly dependent on the eventual goals and on the input data (programs).
Yet another suggestion!
See Larry Lustig's answer! Introspection may simplify much of you task as well. The implication is that you'd need to a) write your logic within each of the the underlying language b) integrate/load the programs to be inspected. All depends, but again, a possible way out from the -let's be fair- relatively heavy investment with formal grammar tools.
For Python, the situation is trivial: there is a Python parser in the standard library as well as a more high-level module for manipulating ASTs.
Also, Python has a somewhat simple grammar (at least if you use the trick to keep an indentation stack in your lexer and inject fake BEGIN and END tokens in your token stream, so that you can treat Python as a simple keyword delimited Algol-like language in your parser), so it is often used as an example grammar for parser generators, which means that you can find literally dozens of Python parsers for pretty much every single parser generator, programming language and platform out there. (E.g., here is a Haskell module implementing a Python lexer and parser.)
For Ruby, there are quite a number of parsers available.
Ruby is incredibly hard to parse, so if you need full fidelity, you pretty much have to use the original YACC grammar file from the YARV Ruby implementation. (parse.y in the top-level source directory.) JRuby's parser is derived from that file, and it is the only one of the implementation parsers that has been explicitly designed to also be used by other clients and not just the interpreter itself. (For example, the Eclipse RDT plugin, the Eclipse DLTK/Ruby plugin, the NetBeans Ruby plugin and the jEdit Ruby syntax highlighting all use JRuby's parser.) To facilitate that, JRuby's parser has actually been repackaged as a separate project.
Of course, there are YACC clones for pretty much every language on the planet. However, be aware that YARV does not use a lex generated scanner. It uses a hand-written scanner in C, and also the YACC grammar contains quite a bit of semantic actions in C. Those parts will have to be re-implemented (like they were in JRuby).
The XRuby compiler is the only full Ruby implementation that does not use YARV's parse.y, it uses an ANTLRv3 grammar and an ANTLRv3 tree grammar that have been developed from scratch. ANTLR can generate parsers for a whole bunch of languages, including for example Java and C#. Its Ruby backend, however, is in dire need of some work.
RedParse is a Ruby parser written in Ruby, which claims to be able to parse all Ruby syntax correctly. It is used, for example, in the YARD Ruby documentation tool to, among other things, extract method names.
ruby_parser is another Ruby parser in Ruby. It is generated from parse.y via the racc parser generator that is part of Ruby's standard library.
YARV actually contains a parser library called ripper, which allows you to parse Ruby code. Unfortunately, it is completely undocumented, so you basically have to figure it out by reading blog posts. Except of course, being undocumented, almost nobody else has figured it out yet, either and written a blog post.
However, for your purposes, you don't actually need a full-blown Ruby parser. You only need enough to extract method names and some other stuff.
RDoc, the Ruby documentation generator, contains a Ruby parser which can parse just enough Ruby to, well, extract method names and some other stuff.
Cardinal is a Ruby implementation for the Parrot Virtual Machine. It does not yet run all of Ruby, but its parser should be powerful enough to support all you need. (The parser is written in the Parrot Grammar Engine, so you will obviously have to run it in Parrot, by for example writing your reporting tool in Perl6.)
tinyrb is another Ruby implementation that does not run full Ruby but contains a better written parser than YARV. In this case, the parser uses Ian Piumarta's leg Parsing Expression Grammar parser generator.
For Ruby and Python, can't you simply introspect the class to learn the name of the methods? You'd have to write the same functionality in each language but (at least in Python) there's hardly anything to it.
The DMS Software Reengineering Toolkit has full, robust C# and Python parsers that automatically build complete ASTs. DMS offers facilities for walking the trees and collecting whatever data you might wish to collect.
Another poster's answer here suggests Ruby is really hard to parse. C++ is also famously hard to parse. DMS has been used to parse some 30 other languages, including full C++ in a number of dialects, so Ruby seems eminently doable. Howeever, DMS doesn't have an off-the-shelf parser for Ruby.
I want to write a translator between two languages, and after some reading on the Internet I've decided to go with ANTLR. I had to learn it from scratch, but besides some trouble with eliminating left recursion everything went fine until now.
However, today some guy told me to check out Happy, a Haskell based parser generator. I have no Haskell knowledge, so I could use some advice, if Happy is indeed better than ANTLR and if it's worth learning it.
Specifically what concerns me is that my translator needs to support macro substitution, which I have no idea yet how to do in ANTLR. Maybe in Happy this is easier to do?
Or if think other parser generators are even better, I'd be glad to hear about them.
People keep believing that if they just get a parser, they've got it made
when building language tools. Thats just wrong. Parsers get you to the foothills
of the Himalayas then you need start climbing seriously.
If you want industrial-strength support for building language translators, see our
DMS Software Reengineering Toolkit. DMS provides
Unicode-based lexers
full context-free parsers (left recursion? No problem! Arbitrary lookahead? No problem. Ambiguous grammars? No problem)
full front ends for C, C#, COBOL, Java, C++, JavaScript, ...
(including full preprocessors for C and C++)
automatic construction of ASTs
support for building symbol tables with arbitrary scoping rules
attribute grammar evaluation, to build analyzers that leverage the tree structure
support for control and data flow analysis (as well realization of this for full C, Java and COBOL),
source-to-source transformations using the syntax of the source AND the target language
AST to source code prettyprinting, to reproduce target language text
Regarding the OP's request to handle macros: our C, COBOL and C++ front ends handle their respective language preprocessing by a) the traditional method of full expansion or b) non-expansion (where practical) to enable post-parsing transformation of the macros themselves. While DMS as a foundation doesn't specifically implement macro processing, it can support the construction and transformation of same.
As an example of a translator built with DMS, see the discussion of
converting
JOVIAL to C for the B-2 bomber. This is 100% translation for > 1 MSLOC of hard
real time code. [It may amuse you to know that we were never allowed to see the actual program being translated (top secret).]. And yes, JOVIAL has a preprocessor, and yes we translated most JOVIAL macros into equivalent C versions.
[Haskell is a cool programming language but it doesn't do anything like this by itself.
This isn't about what's expressible in the language. Its about figuring out what machinery is required to support the task of manipulating programs, and
spending 100 man-years building it.]
I've got a set of function definitions written in a C-like language with some additional keywords that can be put before some arguments(the same way as "unsigned" or "register", for example) and I need to analyze these lines as well as some function stubs and generate actual C code from them.
Is that correct that Flex/Yacc are the most proper way to do it?
Will it be slower than writing a Shell or Python script using regexps(which may become big pain, as I suppose, if the number of additional keywords becomes bigger and their effects would be rather different) provided that I have zero experience with analysers/parsers(though I know how LALR does its job)?
Are there any good materials on Lex/Yacc that cover similar problems? All papers I could find use the same primitive example of a "toy" calculator.
Any help will be appreciated.
ANTLR is commonly used (as are Lex\Yacc).
ANTLR, ANother Tool for Language
Recognition, is a language tool that
provides a framework for constructing
recognizers, interpreters, compilers,
and translators from grammatical
descriptions containing actions in a
variety of target languages.
There is also the Lemon Parser, which features a less restrictive grammar. The down side is you're married to lemon, re-writing a parser's grammar to something else when you discover some limitation sucks. The up side is its really easy to use .. and self contained. You can drop it in tree and not worry about checking for the presence of others.
SQLite3 uses it, as do several other popular projects. I'm not saying use it because SQLite does, but perhaps give it a try if time permits.
That entirely depends on your definition of "effective". If you have all the time of the world, the fastest parser would be a hand-written pull parser. They take a long time to debug and develop but today, no parser generator beats hand-written code in terms of runtime performance.
If you want something that can parse valid C within a week or so, use a parser generator. The code will be fast enough and most parser generators come with a grammar for C already which you can use as a starting point (avoiding 90% of the common mistakes).
Note that regexps are not suitable for parsing recursive structures. This approach would both be slower than using a generator and more error prone than a hand-written pull parser.
actually, it depends how complex is your language and whether it's really close to C or not...
Still, you could use lex as a first step even for regular expression ....
I would go for lex + menhir and o'caml....
but any flex/yacc combination would be fine..
The main problem with regular bison (the gnu implementation of yacc) stems from the C typing.. you have to describe your whole tree (and all the manipulation functions)... Using o'caml would be really easier ...
For what you want to do, our DMS Software Reengineering Toolkit is likely a very effective solution.
DMS is designed specifically to support customer analyzers/code generators of the type you are discussing. It provides very strong facilities for defining arbitrary language parsers/analyzers (tested on 30+ real languages including several complete dialects of C, C++, Java, C#, and COBOL).
DMS automates the construction of ASTs (so you don't have to do anything but get the grammar right to have a usable AST), enables the construction of custom analyses of exactly the pattern-directed inspection you indicated, can construct new C-specific ASTs representing the code you want to generate, and spit them out as compilable C source text. The pre-existing definitions of C for DMS can likely be bent to cover your C-like language.