Visualisation

Toxicity reloaded

28 June 2013

At the moment I’m playing with D3.js trying to recreate some of the polymetric diagrams pioneered in CodeCrawler. (You can see my progress on that over here.) In the process it occurred to me that it should be relatively trivial, using the same tools, to recreate the Toxicity charts we did years ago. Having an HTML5 version would be quite welcome, too, because the original implementation uses Excel features that only work on Windows.

Well, turns out it wasn’t too difficult and the result is available in this Github repository. I like the fact that with HTML we can have much richer tooltips compared to the Excel version. What’s even better, though, is that because it is an HTML solution I can inline the fully interactive chart in this post:

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On sabbatical leave

15 May 2013

Last August I completed my tenth year with ThoughtWorks, and we have a tradition to let people take a three-month long sabbatical leave after ten years. Mine got postponed a bit but now I’m off, until August.

During my leave I’ll take it easy, spend more time with the family, but I’m also going to make some progress on the Softvis project that Jonathan McCracken and I started a long time ago. You can see the first steps here: softvis.github.io. More in three months. Hopefully.

If you are interested in writing up a visualisation and contributing it to the project, please get in touch! Maybe, if we get enough visualisations written up, we’ll publish them in a book.

Metrics Tree Maps

3 May 2010

As a consultant I often find myself in a position where I have to get to know a large existing code base quickly; I need to understand how the code is structured, how well it is written, whether there are any major issues, and if so, whether they are localised or whether they are spread throughout the code base. To get a feeling for the general quality of the code I have found Toxicity charts useful. To understand the structure, Dependency Structure Matrices come in handy. Conceptually somewhere between those two lie metrics tree maps, which I want to write about today.

A metrics tree map visualises the structure of the code by rendering the hierarchical package (namespace) structure as nested rectangles, with parent packages encompassing child packages. The actual display is taken up by the leaves in this structure, the classes. Have a look at the following tree map which shows the JRuby code base, without worrying too much about the “metrics” part yet.

At the top right I have highlighted the org.jruby.compiler package. The tree map shows that this package contains a few classes, such as ASTCompiler and ASTInspector, as well as three subpackages, namely impl, ir, and util, with util for example containing a class called HandleFactory, visible on the far right. (Visible in the full-size version.) In the following I explain how the tree maps visualise metrics, and I will explain how to create such maps from Java source code. As usual, adapting this other programming languages is relatively easy.

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Dependency Structure Matrix

9 April 2010

This is just a quick post to raise awareness for a technique that has been around for a while. In software architecture a Dependency Structure Matrix (DSM) can be used to understand dependencies between groupings of classes, that is packages in Java and namespaces in C#. There are obviously other uses, and this Wikipedia article has more background information.

Returning to classes and packages, the following matrix shows a view of some of the core classes of the Spring framework:

In this typical package DSM the packages are listed on both axes. If a package has dependencies on another package, the number of dependencies is listed at the intersection. The package that has the dependency is on the top, the package that it depends on is on the left. In the example above the matrix shows that there are seven dependencies from the beans.propertyeditors package to the core.io package.

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Making build pain visible

3 November 2009

The practice of continuous integration is gaining widespread adoption and almost every project I was involved in over the past few years used a continuous integration server to maintain an up-to-date view on the status of the build. Developers can look at the status page of the server or use tools such as CCTray and CCMenu to find out whether a recent check-in has broken the build. Some teams also use build lights, like these for example, or other information radiators to make the status of the build visible.

The reason why developers need an up-to-date build status is a common, and good, practice: new check-ins are only allowed when the build is known to be good. If it is broken chances are that someone is trying to fix it and dumping a whole new set of changes onto them would undoubtedly make that task harder. Similarly, when the server is building nobody knows for sure whether the build will succeed, and checking in changes would make fixing the build harder, should it fail.

To recap: the build must be good for a developer to be able to check in. On one of our projects this was becoming a rare occurrence, though. In fairness, the build performed fairly comprehensive checks in a complex integration environment, involving an ESB and an SSO solution. The team had already relegated some long-running tests to a different build stage, and they had split the short build, ie. the build that determines whether check ins are allowed, into five parallel builds, bringing build time down from over 45 to under ten minutes. Still, developers often found themselves waiting in a queue, maintained with post-its on a wall, for a chance to check in their changes. Not only that but everybody felt the situation was getting worse, that the build was broken more often. This was obviously a huge waste and I was keen to make it visible to management using a visualisation.

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