Code for identifying programming language in a text file [closed]_问答_开发者

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i'm supposed to write code which when given a text file (source code) as input will output which programming language is it. This is the most basic definition of the problem. More constraints follow:

I must write this in C++.
A wide variety of languages should be recognized - html, php, perl, ruby, C, C++, Java, C#...
Amount of false positives (wrong recognition) should be low - better to output "unknown" than a wrong result. (it will be in the list of probabilities for example as unknown: 100%, see below)
The output should be a list of probabilities for each language the code knows, so if it knows C, Java and Perl, the output should be for example: C: 70%, Java: 50%, Perl: 30% (note there is no need to have the probabilities sum up to 100%)
It should have a good ratio of accuracy/speed (speed is a bit more favored)

It would be very nice if the code could be written in a way that adding new languages for recognition will be fairly easy and involve just adding "settings/data" for that particular language. I can use anything available - a heuristic, a neural network, black magic. Anything. I'am even allowed to use existing solutions, but: the solution must be free, opensource and allow comme开发者_如何学Gorcial usage. It must come in form of easily integrable source code or as a static library - no DLL. However i prefer writing my own code or just using fragments of another solution, i'm fed up with integrating code of others. Last note: maybe some of you will suggest FANN (fast artificial neural network library) - this is the only thing i cannot use, since this is the thing we use ALREADY and we want to replace that.

Now the question is: how would you handle such a task, what would you do? Any suggestions how to implement this or what to use?

EDIT: based on the comments and answers i must emphasize some things i forgot: speed is very crucial, since this will get thousands of files and is supposed to answer fast, so looking at a thousand files should produce answers for all of them in a few seconds at most (the size of files will be small of course, a few kB each one). So trying to compile each one is out of question. The thing is, that i really want probabilities for each language - so i rather want to know that the file is likely to be C or C++ but that the chance it is a bash script is very low. Due to code obfuscation, comments etc. i think that looking for a 100% accurate code is a bad idea and in fact is not the goal of this.

You have a problem of document classification. I suggest you read about naive bayes classifiers and support vector machines. In the articles there are links to libraries which implement these algorithms and many of them have C++ interfaces.

One simple solution I could think of is that you could just identify the keywords used in different languages. Each identified word would have score +1. Then calculate ratio = identified_words / total_words. The language that gets most score is the winner. Off course there are problems like usage of comments e.t.c. But I think that is a very simple solution that should work in most cases.

If you know that the source files will conform to standards, file extensions are unique to just about every language. I assume that you've already considered this and ruled it out based on some other information.

If you can't use file extensions, the best way would be to find the things between languages that are most different and use those to determine filetype. For example, for loop statement syntax won't vary much between languages, but package include statements should. If you have a file including java.util.*, then you know it's a java file.

I'm sorry but if you have to parse thousands of files, then your best bet is to look at the file extension. Don't over engineer a simple problem, or put burdensome requirements on a simply task.

It sounds like you have thousands of files of source code and you have no idea what programming language they were written in. What kind of programming environment do you work in? (Ruling out the possibility of an artificial homework requirement) I mean one of the basics of software engineering that I can always rely on are that c++ code files have .cpp extension, that java code files have the .java extension, that c code files have the .c extension etc... Is your company playing fast and loose with these standards? If so I would be really worried.

As dmckee suggested, you might want to have a look at the Unix file program, whose source is available. The heuristics used by this utility might be a great source of inspiration. Since it is written in C, I guess that it qualifies for C++. :) You do not get confidence percentages directly, though; maybe are they used internally?

Take a look at nedit. It has a syntax highlighting recognition system, under Syntax Highlighting->Recognition Patterns. You can browse sample recognition patterns here, or download the program and check out the standard ones.

Here's a description of the highlighting system.

Since the list of languages is known upfront you know the syntax/grammar for each of them. Hence you can, as an example, to write a function to extract reserved words from the provided source code.

Build a binary tree that will have all reserved words for all languages that you support. And then just walk that tree with the extracted reserved words from the previous step.

If in the end you only have 1 possibility left - this is your language. If you reach the end of the program too soon - then (from where you stopped) - you can analyse your position on a tree to work out which languages are still the possibitilies.

You can maybe try to think about languages differences and model these with a binary tree, like "is feature X found ? " if yes, proceed in one direction, if not, proceed in another direction.

By constructing this search tree efficiently you could end with a rather fast code.

This one is not fast and may not satisfy your requirements, but just an idea. It should be easy to implement and should give 100% result.

You could try to compile/execute the input text with different compilers/interpreters (opensource or free) and check for errors behind the scene.

The Sequitur algorithm infers context-free grammars from sequences of terminal symbols. Perhaps you could use that to compare against a set of known production rules for each language.