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Tag Cloud Visualization for Source Code

I’ve always been a huge fan of Wordle, so when I saw Fabian Steeg’s announcement of Cloudio – the SWT-based tag cloud visualization for Zest – I knew I wanted to do something with it, so I created Sourcecloud (suggestions for a better name are welcome).

Sourcecloud is an Eclipse plug-in that lets you create tag clouds of your source code. The idea for this project came from Kevlin Henney, who used such tag clouds in a presentation at Jazoon 2010 (if I remember correctly). Luckily for me, Cloudio comes with an example application, from which I was able to reuse most of the parts, so all the credits go to Cloudio’s creator Stephan Schwiebert.

Why would you want to make tag clouds for source code? It can give you a quick first impression of the quality of a code base. Ideally, you should see many names of the project’s domain. On the other hand, if you see lots of nullsints and Strings, chances are that the code will be hard to understand because there are not many domain specific types in it.

You can install Sourcecloud from the update site for integration builds into Eclipse Indigo. And here’s how the result looks:

And here’s a screenshot of the Eclipse view:

The source code is on GitHub, so if you want to add or change something, fork it and send me a pull request!

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Organizing Imports in Scala

Organize Imports was a very often requested feature for the Scala IDE for Eclipse, so I wrote the first very limited version as part of my thesis. It couldn’t do much more than sorting the imports and collapsing them from multiple import statements to a single one. At the beginning of this year, Daniel Ratiu provided a patch that made Organize Imports recognize some of the unused imports.

One limitation we still had was that we didn’t really know the complete set of required imports, but this is required to, for example, replace all wildcard imports with the actually used ones. Another request was that it should be possible to push import statements down to the scope where they are used. For this, we also need to know which parts of the code require which imports.

Yet another motivation to write some code to analyze dependencies was that I want to provide a move refactoring, and for this too we need to know some of the dependencies in the code. But that’s for a future post, back to what we have now:

As you can see, just like the JDT, we can now configure grouping for imports (groups are separated by a blank line), and it’s possible to expand or collapse imports from the same package. The Scalaz or Lift users will likely want to always use wildcard imports on some packages and types, so this is also possible. The JDT has some additional options, but I think I implemented the important ones.

And there’s more! No, not Reversi, but if there are missing imports in the file, Organize Imports will add them for you:

This all is part of the latest beta release of the Scala IDE, so please give it a try and open a ticket if you find a problem.

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Eliminating Pattern Matching

In the last few years, I worked on several Java projects where we transformed and analyzed abstract syntax trees, so when I started learning Scala, pattern-matching quickly became one of my favorite language features. I could never warm up to the visitor pattern, so I was thankful that Scala offered a much more powerful alternative.

What I also liked very much was Scala’s consistent use of the Option type in its standard library. Now, instead of having to read the documentation to find out whether some call could return null, the type checker forced me to handle this where necessary. So a lot of my early Scala code looked as follows:

doSomething() match {
  case Some(value) => Some(doSomethingElse(value))
  case None => None
}

While it’s still possible to get a NPE in Scala, it just doesn’t happen with well-written libraries, simply because there’s no need to ever use null.

(By the way, isn’t it funny that Java forces you to check exceptions but doesn’t help you with the much more common and annoying null problem?)

So yes, in practice, Options do save you from NPEs. Does your code also get smaller (because usually, in Scala it will)? Not if you pattern match on Some/None, all the un-wrapping and lifting is quite verbose.

Nowadays, certainly influenced by all the discussions on monads, I realize that pattern-matching on Option is a very primitive form of abstraction, and instead of the code above I now write:

doSomething() map (value => doSomethingElse(value))

(For those unfamiliar with functional programming, map applies the function to the value inside a Some, and does nothing when called on a None.)

Even better, we can fully automate this refactoring! I’m currently working on a first version in the scala-refactoring library. So far, I’ve implemented the refactoring for map, but there are many more we can do, for example:

  • If the Some case does not construct a Some but calls a function that returns an Option, we use flatMap instead of map.
  • If the Some case evaluates to Boolean and the None case returns false, we can replace it with exists. If Some returns true and None returns false, we can just replace the whole pattern match with isDefined.
  • When the None case is (), we can transform to foreach.

So far we have only looked at Option, but there is more: for example, we could also replace pattern-matching on lists and recursion with folds. I’m sure somebody has already written a paper about such refactorings, but I haven’t found anything yet.

What do you think about eliminating pattern matching? Do you also prefer mapping to explicit pattern-matching?

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