Spice up your unit testing

Writing unit tests shouldn’t be a chore. This article presents six tools (with alternatives) that help to improve your developer experience.

Writing unit tests is an activity every reasonable developer does frequently. While it certainly is a useful thing to do, it shouldn’t be a chore. To help you with the process of creating, running and evaluating unit tests, there are numerous tools and add-ons for every programming language around. This article focusses on improving the developer experience (the counterpart of “user experience”) for Java, JUnit and the Eclipse IDE. I will introduce you to the toolset we are using, which might not be the complete range of tools available.

Creating unit tests

  • MoreUnit – This plugin for Eclipse helps you to organize your unit test classes by maintaining a connection between the test and the production class. This way you’ll always see which classes and methods still lack a corresponding test. You can take shortcuts in the navigation by jumping directly into the test class and back. And if you move one file, MoreUnit will move the other one alongside. It’s a swiss army knife for unit test writers and highly recommended.
  • EqualsVerifier – If you ever wrote a custom implementation of the equals()/hashcode() method pair, you’ll know that it’s not a triviality. What’s even more intimidating is that you probably got it wrong or at least not fully correct. The effects of a flawed equals() method aren’t easily determinable, so this is a uncomfortable situation. Luckily, there is a specialized tool to help you with this task exactly. The EqualsVerifier library tests your custom implementation against all aspects of the art of writing an equals() method with just one line of code.
  • Mockito (and EasyMock) – When dealing with dependencies of classes under test, mock objects can come in handy. But writing them by hand is tedious, boring and error-prone. This is where mock frameworks can help by reducing the setup and verification of a mock object to just a few lines of code. EasyMock is the older of the two projects, but it manages to stay up-to-date by introducing new features and syntax with every release. Mockito has a very elegant and readable syntax and provides a rich feature set. There are other mock frameworks available, too.

Running unit tests

  • InfiniTest (and JUnit Max) – Normally, you have to run the unit tests in your IDE by manually clicking the “run” button or hitting some obscure keyboard shortcut. These two continuous testing tools will run your tests while you still type. This will shorten your test feedback loop to nearly milliseconds after each change. Your safety net was never closer. InfiniTest and JUnit Max are both Eclipse plugins, but the latter costs a small annual fee. It’s written by Kent Beck himself, though.

Evaluating unit tests

  • EclEmma (and Cobertura) – If you want to know about the scope or “coverage” of your tests, you should consult a code coverage tool. Cobertura produces really nice HTML reports for all your statistical needs. EclEmma is an Eclipse plugin that integrates the code coverage tool Emma with Eclipse in the finest way possible. Simply run “coverage as” instead of “run as” and you are done. All the hassle with instrumenting your classes and setting up the classpath in the right order (major hurdles when using cobertura) is dealt with behind the scenes.
  • Jester (and Jumble) – The question “who tests my tests?” is totally legit. And it has an answer: Every mutation testing tool around. For Java and JUnit, there are at least two that do their job properly: Jester works on the source code while Jumble uses the bytecode. Mutation testing injects little changes into your production code to test if your tests catch them. This is a different approach on test coverage that can detect code that is executed but not pinned down by an assertion. While Jester has a great success story to tell, Jumble tends to produce similar results as cobertura’s condition coverage report, at least in my experience.


As you can see, there is a wide range of tools available to improve your efforts to write well-tested software. This list is in no way comprehensive. If you know about a tool that should be mentioned, we would love to read your comment.

Combine cobertura with the awesomeness of crap4j

Want the awesomeness of crap4j without running your tests twice in your build? Just combine it with your cobertura data using crapertura.

You may have heard of crap4j when it was still actively developed. Crap4j is a software metric that points you to “crappy” methods in your projects by combining cyclomatic complexity numbers with test coverage data. The rationale is that overly complex code can only be tamed by rigorous testing or it will quickly reduce to an unmaintainable mess – the feared “rotten code” or “crappy code”, as Alberto Savoia and Bob Evans, the creators of crap4j would put it. The crap4j metric soon became our most important number for every project. It’s highly significant, yet easy to grasp and mandates a healthy coding style.

Some enhancements to crap4j

Crap4j got even better when we developed our own custom enhancements to it, like the CrapMap or the crap4j hudson plugin. We have a tool that formats the crap4j data like cobertura’s report, too.

A minor imperfection

The only thing that always bugged me when using crap4j inside our continuous integration build cycle was that at least half the data was already gathered. Cobertura calculates the code coverage of our tests right before crap4j does the same again. Wouldn’t it be great if the result of the first analysis could be re-used for the crap metric to save effort and time?

Different types of coverage

Soon, I learnt that crap4j uses the “path coverage” to combine it with the complexity of a method. This is perfectly reasonable given that the complexity determines the number of different pathes through the method. Cobertura only determines the “line coverage” and “branch coverage”. As it stands, you can’t use the cobertura data for crap4j because they represent different approaches to measure coverage. That’s still true and probably will be for a long time. But the allurement of the shortcut approach was too high for me to resist. I just tried it out one day to see the real difference.

A different metric

So, here it is, our new metric, heavily inspired by crap4j. I just took the line and branch coverage for every method and multiplied them. If you happen to have a perfect coverage (1.0 on both numbers), it stays perfect. If you only have 75% coverage on both numbers, it will result in a “crapertura coverage” of 56,25%. Then I fed this new coverage data into crap4j and compared the result with the original data. Well, it works on my project.

Presenting crapertura

Encouraged by this result, I wrote a complete ant task that acts similar to the original crap4j ant task. You can nearly use it as a drop-in replacement, given that the cobertura XML report file is already present. Here is an example ant call:


It will output the usual crap4j report files to the given target directory. Please note that even if it looks like crap4j data, it’s a different metric and should be treated as such. Therefore, online comparison of numbers is disabled.

The whole project is published on github. Feel free to browse the code and compile it for yourself. If you want a binary release, you might grab the latest jar from our download server.

The complete usage guide can be found on the github page or inside the project. If you have questions or issues, please use the comment section here.


If crapertura is able to give you nearly the numbers that crap4j gave you is up to your project, really. Our test project contained over 20k methods, but very little crap. The difference between crap4j and crapertura was negligible. Both metrics basically identified the same methods as being crappy. Your mileage may vary, though. If that’s the case, let us know. If your experience is like ours, you’ve just saved some time in your build cycle without sacrificing quality.

Open Source Love Day September 2010

Our Open Source Love Day for September 2010 brought love for the cmake hudson plugin and a brand new tool around crap4j that will be released soon.

On wednesday last week, we held our Open Source Love Day for September 2010. Our day started with the usual Homepage Comittee meeting and very soon, we were up and working. This time, our success rate wasn’t as high as we wanted, mostly because we worked on internal tools that didn’t work out quite as well as expected. But, we managed to produce something valuable this day.

The Open Source Love Day

We introduced a monthly Open Source Love Day (OSLD) to show our appreciation to the Open Source software ecosystem and to donate back. We heavily rely on Open Source software for our projects. We would be honored if you find our contributions useful. Check out our first OSLD blog posting for details on the event itself.

On this OSLD, we accomplished the following tasks:

  • A new version 1.6.1 of the cmakebuilder hudson plugin was published. This version consisted of bugfixes only and right now, it still seems flawed. We are working on the issue, expect a new version 1.6.2 soon.
  • Our internal time-tracking tool got love at several issues. One issue required the use of triangle-shaping CSS, as described in this blog post from Jon Rohan. Our issue weren’t finished because Javascript code can rot into a big pile of crap really quick.
  • We managed to make a long hatched dream come true at this OSLD. As you might be aware, we are big fanboys of crap4j, a metric tool that associates test coverage with code complexity. Thus, we wrote the crap4j hudson plugin, release the CrapMap and use some internal improvements, too. The main disadvantage of crap4j is the strong dependency on a specific test coverage tool. Our goal was to use the test coverage data we already collect using Cobertura. We achieved this goal and got the whole thing working. It will be released in the next few weeks, with a detailed blog post here. Stay tuned for this new tool (it already has a name: Crapertura).

What were our lessons learnt today?

  • No matter how clever you are, Javascript outsmarts you every time it appears in superior numbers. Refactoring is the key here, but difficult and tedious to apply.
  • When you dissect a foreign API or code base, you just need to find the right grip. I cannot decribe it more precise right now, but this grip is all you need to open up the code. When playing around with the crap4j code base, as soon as we held the grip, everything else followed naturally. Perhaps “the grip” can be translated with “catching the author’s intent”. These are always magical moments.

Retrospective of the OSLD

This OSLD wasn’t as successful as we wished, partly because of missing manpower (honeymoon holiday!) and because of our inability to tame a Javascript code base. We have to work on our expertise here and we are glad that we’ve found out at an OSLD, not a time- and mission-critical customer project.