If you are somebody who writes code you probably know that moment when you look at some code you didn’t write, or some code you wrote a long time ago, and you think “that doesn’t look good.” Ok, more realistically, you probably think “WTF? I wouldn’t want to touch that with a barge-pole!” It is not even so much about whether the code does what it should do—that takes a bit longer to figure out—or whether the code is too slow. Even if it’s perfectly bug free and performs well, there’s something to the way it’s written. This is part of the internal quality of a software system, something that the users and development managers can’t observe directly; yet, it still affects them because code with poor internal quality is hard to maintain and extend.

Now, as a developer, how do you help managers and business people understand the internal quality of code? They generally want a bit more than “it’s horrible” before they prioritise cleaning up the code over implementing new features that directly deliver business value. Or even: how do you figure out for yourself how bad some code actually is in relation to some other code? These were questions that Chris Brown, Darren Hobbs, and myself were asking ourselves a couple of years ago.

The answer came in the form of a simple bar chart, arguably not the most sophisticated visualisation but a very effective one. And our colleague Ross Pettit had the perfect name for it: The Toxicity Chart. Read on to see what it is and how it’s created.

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One of my favourite tools to render graphs is GraphViz Dot and in an earlier entry I described how to use it to visualise Spring contexts. Today I want to showcase a different application.

Call graphs show how methods call each other, which can be useful for a variety of reasons. The example I use here is the graph rooted in a unit test suite, and in this case the graph gives an understanding of how localised the unit tests are, how much they are real unit tests or how close they are to mini-integration tests. In an ideal case the test method should call the method under test and nothing else. However, even with mock objects that’s not always practical. And if, like myself, you fall into the classicist camp of unit testers, as described by Martin Fowler in Mocks aren’t Stubs, you might actually not be too fussed about a few objects being involved in a single test. In either case, looking at the call graph shows you exactly which methods are covered by which unit tests.

There are several ways to generate calls graphs and I’m opting for dynamic analysis, which simply records the call graph while the code is being executed. A good theoretical reason is that dynamic analysis can handle polymorphism but a more practical reason is that it’s actually really easy to do dynamic analysis; provided you use the right tools. The approach I describe in this article uses Eclipse AJDT to run the unit tests with a simple Java aspect that records the call graph and writes it out into a format that can be rendered more or less directly with Dot. Of course, this technique is not limited to creating graphs for unit test; it only depends on weaving an AspectJ aspect into a Java application.

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The Spring framework has become ubiquitous in the Java world, and there are a large number of tools supporting developers of Spring-based applications. In this post I describe SpringViz; or, more accurately, my variant of it.

SpringViz helps developers with what is at the heart of a Spring-based application, the container and the contexts files that describe the beans. In larger projects these context files can grow quite a bit. Newer versions of the Spring framework introduced features that help reduce the clutter and there are vast numbers of blog posts voicing different opinions on what should and what shouldn’t be in a context file but, no matter what, the number of beans in the context files will grow with the size of a project, and at some point it becomes difficult to understand the overall structure. This is no different from trying to maintain an understanding of a large codebase. In fact, I consider the context files to be code rather than configuration.

I’ve argued before (here and here for example) that to deal with the complexity and sheer size of software systems we need a 1000ft view. This is a view that uses visualisation techniques to aggregate large amounts of data and multiple metrics into one big picture. SpringViz provides that 1000ft view for Spring context files.

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For a few releases the Apple development tools have included OCUnit and many developers have now started to write unit tests. There are lots of tutorials that explain how this is done for the straight-forward cases but there’s one area of testing that has proven difficult on most platforms, and that is testing of the user interface. That said, there are a few things that make this an easier problem to solve with Cocoa and in this post I’ll explain why.

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Another major improvement of OCMock: it now supports more flexible constraints on the expected arguments. This is done in the Objective-C way and user-defined constraints don’t have to implement a formal interface, they’re just methods in the test class. As usual this release also includes several contributions from the community. More details on the OCMock page at Mulle Kybernetik.