Ruby Weekly is a weekly newsletter covering the latest Ruby and Rails news.

SourceClassifier: Identifying Programming Languages Quickly

By Peter Cooper / January 5, 2009

If you're developing a snippets or pastie-type system or another form of CMS where source code might be stored, it'd be incredibly useful to automatically detect what language a provided source is in so that you can style it appropriately.

Chris Lowis' SourceClassifier (or Github repo) library does just that, using a Bayesian classifier trained on source code from the Alioth Shootouts. Out of the box it has support for C, Java, JavaScript, Perl, Python and Ruby, but you can train it to recognize others (CSS and HTML seem like notable omissions to me).


  1. Ray says:

    CSS and HTML are omitted because they aren't programming languages.

  2. Peter Cooper says:

    Strictly true, but for many of the places you'd use something like SourceClassifier - like a pastie site - CSS and HTML are considered "languages" (or at least have their own formatting conventions) in a pragmatic sense.

  3. Chris says:

    Thanks for the mention Peter! Good point about HTML and CSS, they certainly fit the use case for the gem - even if they're not, as pointed out, programming languages.

    I think csszengarden is a good corpus for css files, and I'll try to find a suitable one for HTML, maybe I'll stick to semantically correct/valid XHTML for simplicity! Good suggestion, I'll add these to v0.3 of the gem.

  4. BJ says:

    Seems odd that PHP isn't on the list since it is one of the most ubiquitous web languages out there. Great resource though, keep it up.

  5. Apostlion says:

    Bayesian classifier can be cool enough for languages that are more-or-less syntax identical (LISP varieties or, say, Python and Ruby), but is the real-world performance really trivial in this case?

    I'm pretty sure a rule-based pre-filter would do a pretty good job to sort out main syntax families, to be then rammed with a Bayesian approach.

  6. railsjedi says:

    Needs to be added to stat! Looking forward to it

  7. Chris says:

    Thanks for all your comments. I've updated the gem to recognise PHP and CSS (trained on examples from I'm still looking for a suitable corpus of valid (X)HTML.

    @railsjedi - that'd be great!

    @Apostlion - do you mean training performance? Once trained the training file can be kept in memory. Perhaps it'd be useful to run some benchmarks on performance of recognition, I'll add it to the TODO list. Rules-based filters for this task do also exist, however the classifier approach uses a small amount of code and is trivial to extend to new languages. I think both approaches have their merits.

  8. Apostlion says:

    @Chris -

    I wasn't exactly referring to performance, more like training material bias. Say, (for a trivial example), that a Shootout Python scripts were mostly written by a structural programming fan, and Ruby scripts were mostly written by a functional programming fan.

    Then, the Bayesian classifier may assume that, say, if...else construct is a Python give-away, while lambda is a Ruby construct — even though both obviously are present in both languages, and downplay the ‘real’ differences — such as ubiquitous end's in Ruby scripts.

Other Posts to Enjoy

Twitter Mentions