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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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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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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Over the past years I have shown everyone who could not run fast enough some of the tools based on Moose. And even now I cannot resist putting a screenshot of CodeCity into this post.

Most of the Moose tools now use the MSE file format as an interchange format. By the way, if you are interested in writing your own visualisations or analysis tools it is probably worthwhile looking at MSE, reading this format is so much more convenient than parsing source code.

In Java it was always relatively easy to create MSE files. Among many other things, iPlasma can read Java source code and export to MSE. That said, iPlasma has so many interesting features itself that oftentimes no export to an external tool is necessary.

For C# the story was different and for one reason or another no tool existed that could create MSE files for C#. This has changed now. As a student project at the University of Lugano such a tool was written and, thanks to Michele Lanza, then donated for general use. I’ve made a few improvements and put the code into this Bitbucket repository.

At some point last year I was asked to review the architecture of the software behind a large and popular website. The resident architect explained how he had followed a modern approach, decoupling the web front-end from back-end services that provide content. To gain further flexibility he had put the front-end and the services on an ESB, mostly to cater for the possibility to make the content available to other consumers. In short, the architecture diagram looked a lot like many others: nothing to see here, move on.

The diagram above only shows one of the content services, which for the sake of this article is a service that provides contact details for a person.

Based on conversations with the project sponsors I began to suspect that at least the introduction of the ESB was a case of RDD, ie. Resume-Driven Development, development in which key choices are made with only one question in mind: how good does it look on my CV? Talking to the developers I learned that the ESB had introduced “nothing but pain.” But how could something as simple as the architecture in the above diagram cause such pain to the developers? Was this really another case of architect’s dream, developer’s nightmare?

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