Clean Code

This article recaps how to identify some of the most common code smells using NDepend. The basis of this series is Object-Oriented Metrics in Practice, by Michele Lanza and Radu Marinescu. This book describes (among other things) how you can use several targeted metrics to implement detection strategies for identifying design disaharmonies (code smells). You can read a summary of the book and my review in this article.

Detection Strategies

The design disharmonies are split into three categories: Identity Disharmonies, Collaboration Disharmonies and Classification Disharmonies. A Detection Strategy is a composed logical condition, based on a set of metrics for filtering.

Identity Disharmonies

Identity disharmonies affect methods and classes. These can be identified by looking at an element in isolation.

Collaboration Disharmonies

Collaboration Disharmonies affect the way several entities collaborate to perform a specific functionality.

Classification Disharmonies

Classification Disharmonies affect hierarchies of classes.

Conclusion

These detection strategies identify potential culprits. You need to analyze the candidates and decide if it’s an issue or just a false positive. I ended up adding some more project specific filters to ignore most of the false positives. Adding some basic where clause which exclude certain namespace or class name patterns can get you a long way. But, of course, these depend on your specific project and conventions. The beauty of NDepend is that you can update the queries as you wish: add filters, play with the thresholds or add more conditions.

Analyzing a suspect can be done in code, but you can also use other tools. NDepend has some views that can help you with the investigation: Treemaps, Dependency Graph, Dependency Structure Matrix, query results. In Object-Oriented Metrics in Practice the authors use Class Blueprints, but I don’t know a tool that can generate these views for .Net code.

After identifying the issues, you can start refactoring. For some strategies on how to tackle each disharmony or how to prioritize them, I recommend reading the book.

Clean Code

In the previous blog post we have seen how to detect potential God Classes with NDepend. In this article we’ll see how to detect methods that suffer from Feature Envy.

Feature Envy Detection Strategy

The feature envy code smell refers to methods that access data from other sources, rather than their own. Object-Oriented Metrics in Practice, by Michele Lanza and Radu Marinescu, proposes the following detection strategy for Feature Envy:

(ATFD > Few) AND (LAA < One Third) AND (FDP <= Few)

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