visualization – Simple Oriented Architecture

Architecture, Quality

Defining Test Boundaries – An example

I think that everybody agrees that testing is required in order to build a quality product. But there’s also a lot of confusion about the boundaries of each test type. What’s the scope of a unit test? What’s the difference between an integration test, an integrated test and a contract test? If you ask 3 developers about test boundaries, you’ll most likely get 3 different answers. For example, I still talk to people who consider that a unit test should test a single class/method.

What’s clear is that most teams don’t have a consensus on what’s the scope of the different types of automated tests and the differences between them. Getting to a universal consensus might be hard, but getting to a consensus inside the team should be easy enough. In this blog post we’ll see an example of how to do that.

Books, Quality

Book Review: Your Code as a Crime Scene

In the last couple of months I’ve been learning about what information can I extract from a codebase. I’ve written some articles on how to use NDepend to extract a static view of the system’s quality. But this view is based only on the current state of the codebase. What about source code history? What can it tell us? How has the code changed? These are exactly the kind of questions that Adam Thornhill‘s book, Your Code as a Crime Scene: Use Forensic Techniques to Arrest Defects, Bottlenecks, and Bad Design in Your Programs, tries to answer.

Books, Quality

Book Review: Object-Oriented Metrics in Practice

I have been using static code analyzers for a while now. While these are useful, you need to spend a lot of time analyzing warnings and issues. And the problem is that, after you first run one of the static code analysis tools on a legacy project, you are overwhelmed by the number of issues. Object-Oriented Metrics in Practice, by Michele Lanza and Radu Marinescu, shows us how to use metrics effectively. It shows how to combine metrics in order to spot design flaws. This book also presents some novel visualization techniques. These are a great way to understand and visualize a complex system.

Quality

Visualizing Code Metrics with NDepend

Lines of code, cyclomatic complexity, coupling, cohesion, code coverage. You’ve probably heard about these metrics before. But do you actively track them? Should you? Visual Studio computes some of these metrics out of the box. But if you want to define a custom metric, you’re out of luck. Yet, there are a bunch of code metrics that you might find useful for your code base. More so, a composite metric might be more helpful than the sum of its parts. For example, the C.R.A.P. Metric detects complex code that is not covered by unit tests. How can you track such a metric in Visual Studio? In this article we’ll see how to visualize code metrics, add custom metrics and how to monitor trends with NDepend.

Code Metrics

NDepend computes many metrics out of the box. You can use the intellisense support to discover the standard metrics for a given code element:

But it would be hard to extract information from these metrics if all we got was a bunch of numbers. We need other techniques to help us break down the complexity of the data. Visualization techniques complement metrics, by making it easier to synthesize and digest this information.