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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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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