I am trying to research on the possible parsers, as part of developing a PC application that can be used for parsing a Lin Descriptor File. The current parser application is based on flex-bison parsing approach. Now I need to redesign the parser, since the current one is incapable of detecting specific errors.
I have previously used Ragel parser(https://en.wikipedia.org/wiki/Ragel) for parsing a regular expression (Regex : https://en.wikipedia.org/wiki/Regular_expression) commands and it proved very handy.
However, with the current complexity of a LDF-file, I am unsure if Ragel(with C++ as host language) is the best possible approach to parse the LDF-file. The reason for this is that the LDF-file has a lot of data that is not fixed or constant, but varies as per the vendors. Also the LDF fields must have retain references to other fields to detect errors in the file.
Ragel is more suited when the structure for parsing is fixed(thats what I found while developing a Regex parser)
Could anyone who has already worked on such a project, provide some tips to select a suitable parser for the Lin Descriptor File.
Example for Lin Descriptor File : http://microchipdeveloper.com/lin:protocol-app-ldf
If you feel that an LALR(1) parser is not adequate to your parsing problem, then it is not possible that a finite automaton would be better. The FA is strictly less powerful.
But without knowing much about the nature of the checks you want to implement, I'm pretty sure that the appropriate strategy is to parse the file into some simple hierarchical data structure (i.e. a tree of some form, usually called an AST in parsing literature) using a flex/bison grammar, and then walk the resulting data structure to perform whatever semantic checks seem necessary.
Attempting to do semantic checks while parsing usually leads to over-complicated, badly-factored and unscalable solutions. That is not a problem with the bison tool, but rather with a particular style of using it which does not take into account what we have learned about the importance of separation of concerns.
Refactoring your existing grammar so that it uses "just a grammar" -- that is, so that it just generates the required semantic representation -- is probably a much simpler task than reimplementing with some other parser generator (which is unlikely to provide any real advantage, in any case).
And you should definitely resist the temptation to abandon parser generators in favour of an even less modular solution: you might succeed in building something, but the probability is that the result will be even less maintainable and extensible than what you currently have.
Related
I am actually trying to make a simple chatbot for information retrieval purposes in slack using python and I have come up with a Context Free Grammar (CFG) for synatx check . Now that I have a grammar, I want to create a parsing table/ parse tree for this grammar to validate my input string. It would be really helpful if you could let me know some libraries/ links/ mateirals that can help me implement a parser to perform syntax check for my chatbot.
Any help is appreciated. Thanks.
If you already wrote a context-free grammar, you can use the ChartParser of NLTK to parse any input, as described here: http://www.nltk.org/book/ch08.html
I think, however, that a hand written grammar will not be robust enough to deal with the huge amount of variations your users could write. These have gone out of fashion decades ago due to their poor performance and in constituency parsing one rather uses treebanks to generate grammars.
Depending on what exactly you want to archive, I suggest you also take a look at a dependency parser e.g. from spaCy. They are faster and allow you to easily navigate from the predicate of the sentence to its subject and objects.
I'm looking for steps/libraries/approaches to solve this Problem statement.
Given a source file of a Programming language, I need to parse it and Subdivide it into components.
Example:
Given a Java File, I need to find the following in it.
list of Imports
Classes present in it
Attributes in the Class
Methods in it - along the Parameters if any.
etc.
I need to extract these and store it separately.
Reason Why I want to do it?
I want to build an Inverted Index on the top of these Components.
Example queries to Inverted index
1. Find the list of files with Class name: Sample
2. Find the positions where variable XXX is used within the class AAA.
I need to support queries likes the above
So, my plan is given a file, if I build these components from it, It would be easy to build an Inverted index on the top of it.
Example: Sample -- Class - Sample.java(Keyword - Component - FileName )
I want to build an Inverted index like above.
I see it is being implemented in many IDEs like IntelliJ.What I'm interested it how much effort it would take to build something like this. And I want to try implementing the same for at least one language.
Thanks in advance.
You can try to do this "just" a parser; for your specific example, that might be enough.
But you'll need a parser for each language. If you stick to just Java, you can find Java parsers pretty easily; just reuse one, there is little point in you reinventing one more set of grammar rules to describe Java.
For more than one language, this starts to get tricky. You can:
try to find a separate parser for each language. This may be sort of successful for mainstream languages. As you get to less well known languages, these get a lot harder to find. If you succeed, you'll have the problem that the parsers are likely incompatible technology; now gluing them together to collectively collect your index information is going to be a mess.
pick one parsing technology and get grammars for all the languages you care about. You have only two realistic choices: YACC/Bison, and ANTLR.
As a practical matter the YACC and Bison have been used to implement LOTS of languages... but the grammar files are not collected in one place, so they are hard to find. ANTLR at least has a single repository you can find at their web site. So that might kind of work.
Its going to be quite the effort to assemble all these into an integrated whole.
A complication is that you may want more than just raw syntax; you might want to know the meaning of the symbols, and for each symbol, precisely where it is defined in which file. After all, you want your index to be accurate at scale, and this will require differentiating foo the variable name from foo the function name. Arguably you need symbol tables.
As a general rule, this is where pure-parsing of languages breaks down;
there is serious Life After Parsing.
In that case, you want an integrated set of tools for extracting information from the different languages.
Our DMS Software Reengineering Toolkit is such a framework, and has some 40 languages predefined for it. We use something like OP's suggested process to build indexes of a code base for search tools based on DMS. Building something like DMS is an enormous effort.
and also I want to compare the result of concurrent parsing with serial parsing .
How to do this? Which one is the simplest approach?
As others have said, YACC (based on LR parsing) by itself isn't "concurrent".
(It is my understanding that YACC isn't even re-entrant, which will make it pretty hard to use it in multithreaded context no matter what you do. The non-reentrancy can presumably be overcome with mere sweat, so this is an annoyance, not a show stopper.)
One idea is to construct a pipeline, allowing a lexer to generate lexemes into a steam as fast as it can, and letting the actual parser read from the stream. This can get you at best a factor of 2. You might be able to do this with YACC relatively easily, modulo setting up communicating threads.
McKeeman et al have implemented parallel LR parsing by dividing the file into N chunks for N threads and claim to have gotten good results. The approach isn't simple, because dividing parsing of a single file into parallel chunks of about the same size, and stitching those chunks together, isn't easy. I doubt that one could easily hack up YACC to do this.
A screwy idea is parse a file from both ends toward the middle.
Its easy enough to define the backward parser grammar from the "natural" forward one: just reverse all the grammar rule content. Nothing is easy; this might introduce ambiguities not present in the forward parser. This paper combines McKeeman's idea of breaking the file into chunks with bidirectional parsing of each chunk, enabling one to find a lot of parallelism on a big file.
Easier to do is to parallelize the parsing of individual files, using whatever parsing technology you have. This does parallelize relatively well, although the parsing time for individual files may not be even, so this is best done with some kind of worklist and a team parser threads taking work from this worklist. You could probably organize to do this with YACC.
Our DMS Software Reengineering Toolkit (DMS) uses a generalization of this when reading large systems of Java sources and classes. Parsing by itself isnt very useful; you almost always need to build symbol tables, too. DMS reading Java thus parallelizes both parsing, AST building and symbol table construction. Starting from a base set of filenames, it launches parses in parallel grains (multiplexed by DMS on top of OS threads); when a parser completes, the parsed tree is handed to name resolver, which splits into a parallel grain per parallel scope encountered. Nested scopes cause a tree of grains to be generated. Further work is gated by treating resolution of a scope as a future (event); while the scope is being resolved, more Java files parse/name resolution activities may be launched; when a scope is resolved, an event is signalled and grains waiting for scope completion can then inspect the scope content to resolve their own names. The tangle of (potential) parallelism in the middle of this is almost frightening :-} but is managed by DMS's underlying parallel programming language, PARLANSE, which uses work-stealing to balance the load across the threads.
Experience shows that doing this in production with 8 cores leads to a 5x speedup over sequential for a few thousand (typical/Java implies small) source files. We think we know where the bottleneck is; there are parts of the name resolver that are more expensive than it should be in terms of excess synchronization in an attribute grammar. I suspect we can get this closer to 8. So, parallel parsing and name resolution can be pretty effective.
We don't do quite as well with C++14, because of all the dependencies of individual files on #includes that it reads, often in different preprocessor configurations.
If the input allows splitting into chunks, like, e.g. log file lines, which can be parsed in parallel, then you can parse a certain number such chunks in parallel using a producer/consumer queue and then join the parse results.
Out of curiosity, I wonder what can people do with parsers, how they are applied, and what do people usually create with it?
I know it's widely used in programming language industry, however I think this is just a tiny portion of it, right?
Besides special-purpose languages, my most ambitious use of a parser generator yet (with good old yacc back in C, and again later with pyparsing in Python) was to extract, validate and possibly alter certain meta-info from SQL queries -- parsing SQL properly is a real challenge (especially if you hope to support more than one dialect!-), a parser generator (and a lexer it sits on top of) at least remove THAT part of the job!-)
They are used to parse text....
To give a more concrete example, where I work we use lexx/yacc to parse strings coming over sockets.
Also from the name it should give you an idea what javacc is used for (java compiler compiler!)
Generally to parse Domain Specific Languages or scripting languages, or similar support for code snipits.
Previously I have seen it used to parse the command line based output of another software tool. This way the outer tool (VPN software) could re-use the base router IPSec code without modification. As lots of what was being parsed was IP Route tables and other structured repeated text.
Using a parser allowed simple changes when the formatting changed, instead of trying to find and tweak the a hand written parser. And the output did change a few times of the life of the product.
I used parsers to help process +/- 800 Clipper source files into similar PRGs that could be compiled with Alaksa Xbase 32.
You can use it to extend your favorite language by getting its language definition from their repository and then adding what you've always wanted to have. You can pass the regular syntax to your application and handle the extension in your own program.
I'm creating a compiler with Lex and YACC (actually Flex and Bison). The language allows unlimited forward references to any symbol (like C#). The problem is that it's impossible to parse the language without knowing what an identifier is.
The only solution I know of is to lex the entire source, and then do a "breadth-first" parse, so higher level things like class declarations and function declarations get parsed before the functions that use them. However, this would take a large amount of memory for big files, and it would be hard to handle with YACC (I would have to create separate grammars for each type of declaration/body). I would also have to hand-write the lexer (which is not that much of a problem).
I don't care a whole lot about efficiency (although it still is important), because I'm going to rewrite the compiler in itself once I finish it, but I want that version to be fast (so if there are any fast general techniques that can't be done in Lex/YACC but can be done by hand, please suggest them also). So right now, ease of development is the most important factor.
Are there any good solutions to this problem? How is this usually done in compilers for languages like C# or Java?
It's entirely possible to parse it. Although there is an ambiguity between identifiers and keywords, lex will happily cope with that by giving the keywords priority.
I don't see what other problems there are. You don't need to determine if identifiers are valid during the parsing stage. You are constructing either a parse tree or an abstract syntax tree (the difference is subtle, but irrelevant for the purposes of this discussion) as you parse. After that you build your nested symbol table structures by performing a pass over the AST you generated during the parse. Then you do another pass over the AST to check that identifiers used are valid. Follow this with one or more additional parses over the AST to generate the output code, or some other intermediate datastructure and you're done!
EDIT: If you want to see how it's done, check the source code for the Mono C# compiler. This is actually written in C# rather than C or C++, but it does use .NET port of Jay which is very similar to yacc.
One option is to deal with forward references by just scanning and caching tokens till you hit something you know how to real with (sort of like "panic-mode" error recovery). Once you have run thought the full file, go back and try to re parse the bits that didn't parse before.
As to having to hand write the lexer; don't, use lex to generate a normal parser and just read from it via a hand written shim that lets you go back and feed the parser from a cache as well as what lex makes.
As to making several grammars, a little fun with a preprocessor on the yacc file and you should be able to make them all out of the same original source