Books, Quality

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.

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The Overview Pyramid describes and characterizes the structure of an object oriented system by looking at three main areas: size and complexity, coupling and inheritance. This visualization technique has been defined by Radu Marinescu and Michele Lanza in their book Object-Oriented Metrics in Practice. In this blog post we’ll see how to compute all the necessary metrics by using NDepend.

The Overview Pyramid

Example Overview Pyramid

The purpose of the Overview Pyramid is to provide a high level overview of any object oriented system. It does this by putting in one place some of the most important measurements about a software system. The left part describes the Size and Complexity. The right part describes the System Coupling. The top part describes the inheritance usage.

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Books, Quality

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.

Object-Oriented Metrics in Practice

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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:

Computer Code Metrics

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.

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Your code base has a lot to tell you. The question is: How can you listen to it? You can identify code smells when you’re reading code or extending it, but this doesn’t give you an overview. After you have been working for a while in a project, you can name some of its strengths and weaknesses. But this approach takes a long time, relies on experience and is subjective. It would be nice if you could query a code base in a structured way. Basic search functionality from an IDE like Visual Studio isn’t powerful enough. NDepend allows you to query .Net code using LINQ syntax through CQLinq – Code Query LINQ. In this blog post we’ll discuss how to query your code base using CQLinq.

An Example

Since I like to learn from examples, let’s first see a simple, yet very powerful query. The following code detects classes that are candidates to be turned into structures. This is one of the default CQLinq rules.

from t in JustMyCode.Types where
  t.IsClass &&
 !t.IsGeneratedByCompiler &&
 !t.IsStatic &&
  t.SizeOfInst > 0 &&

  // Structure instance must not be too big,
  // else it degrades performance.
  t.SizeOfInst <= 16 &&
  // Must not have children
  t.NbChildren == 0 &&

  // Must not implement interfaces to avoid boxing mismatch
  // when structures implements interfaces.
  t.InterfacesImplemented.Count() == 0 &&

  // Must derive directly from System.Object
  t.DepthOfDeriveFrom("System.Object".AllowNoMatch()) == 1 &&

  // Structures should be immutable type.
select new { t, t.SizeOfInst, t.InstanceFields }

As you can see, the query is quite readable. Even if you don’t know the CQLinq syntax, you understand what it does. And since it’s based on Linq, it already seems familiar.

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Clean Code, Quality

How do you manage dependencies in your project? Since an image speaks a thousand words, I’ve always been a fan of visual management. Unfortunately, Visual Studio Professional doesn’t provide a way to do this. In the Premium and Enterprise editions you can visualize code dependencies on dependency graphs. But I don’t think this is enough. An architectural diagram with every assembly or namespace in my solution doesn’t tell me that much. It contains too much information.

Fortunately, there is a tool that can help you manage dependencies in the .Net world: NDepend (there is also a Java port – JArchitect). NDepend is a static analysis tool that, among other things, allows you to visualize dependencies. After I first ran NDepend on a project, I was overwhelmed with information. Then I took some time to play around and discover what can it tell me about my solution. NDepend integrates into Visual Studio quite nicely and points you in the right direction through tool tips and links. This is useful for people who prefer learning by doing. Aside from giving you information, it also tells you what to do with that information.

NDepend has two main views for managing dependencies: the Dependency Graph and the Dependency Structure Matrix. Apart from these, there is also an Abstractness vs Instability report that can be helpful. In this blog post, we’ll discuss some of the things that these views can tell you about your solution.

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Quality, Specification by Example

I have been using Specification by Example (a.k.a BDD, ATDD) for the last couple of years. This has helped bridge the gap between technical people and business people. It has also helped ramp up new members on our team, since we have a living documentation of the system. This isn’t always easy and we’re continuously looking for ways of improving the structure of our BDD specification files.  There are some questions that help us spot improvement points:

  • How easy is to have an overview of what the product does?
  • What are the main business areas of the product?
  • How easy is to find a specification?
  • How easy is to find related specifications?
  • How does this feature relate to that feature?
  • If you just point a new joiner to the specifications folder, will she have a decent idea of what the system does?

In this blog post I hope to give you a few tips that might help answer some of these questions. These aren’t new ideas, but I find them pretty effective. Continue Reading


In a previous blog post we discussed why building the right product is hard and some tips on how to achieve a high perceived integrity. But if you’re building a strategic solution that should support your business for many years, this is not enough. With time, new requirements get added, features change and team members might leave the project. This, together with hard deadlines, means that technical debt starts to incur, and the price of adding new features increases until someone says it will be easier to rebuild the whole thing from scratch. This isn’t a situation you’d like to be in, so that’s why it is important to build the product right.

Building the product right

In their book, Mary and Tom Poppendieck define this dimension of quality as the conceptual integrity of a product. Conceptual (internal) integrity means that the system’s central concepts work together as a smooth, cohesive whole.

How can you maintain the conceptual integrity of a product during its lifetime? You rely on communication, short feedback loops, transparency and empowered teams. These are the same principles that can lead to a high perceived integrity. The only difference is that you apply them at an architectural and code level. Continue Reading


If you ask a hundred developers to define software quality, you’ll probably get a hundred different answers. There are a lot of ways to categorize quality, but one that I find most useful is building the right product and building the product right.

Building the Right Product

First we have to make sure we are building the right product. The most performant and secure product, having the cleanest and most extensible architecture, covered with unit tests and acceptance tests is in vain if nobody uses it.

In their book, Lean Software Development: An Agile Toolkit, Mary and Tom Poppendieck define this dimension of quality as the perceived integrity of a product. Perceived (external) integrity means the totality of the product achieves a balance of function, usability, reliability, and economy that delights customers.

Traditionally, when customers want to build a product, they talk with business analysts and write down the requirements. These documents are then handed over to architects, who then define the high level architecture and pass the design documents down to programmers who start implementing. There’s a gap between each step and as we go through the process, we lose more and more information and our chances of building the right product get slimmer.

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