In the previous articles in this series we’ve seen:
In this article we’ll see how to identify the Brain Method code smell.
Brain Methods are methods that centralize the intelligence of a class. Object-Oriented Metrics in Practice, by Michele Lanza and Radu Marinescu, proposes the following detection strategy for Brain Methods:
(LOC > HighLocForClass/2) AND (CYCLO >= High) AND (MAXNESTING >= Several) AND (NOAV > Many)
In the previous articles in this series we’ve seen:
In this article we’ll see how to identify the Data Class code smell.
Data Classes are classes that expose their data directly and have few functional methods. Object-Oriented Metrics in Practice, by Michele Lanza and Radu Marinescu, proposes the following detection strategy for Data Classes:
(WOC < One Thrid) AND
(((NOPA + NOAM > Few) AND (WMC < High)) OR
((NOPA + NOAM > Many) AND (WMC < Very High)))
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.
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)
Most of the static analysis tool in the .Net world report isolated code metrics. While this is useful, I would like to be able to detect coarse grained code smells. Being able to correlate several metrics to identify design disharmonies allows you to treat a problem holistically. In this blog post we’ll see how we can use NDepend to detect potential God Classes.
A God Class is a class that centralizes the intelligence in the system. It is large, complex, has low cohesion and uses data from other classes. Object-Oriented Metrics in Practice, by Michele Lanza and Radu Marinescu, proposes the following detection strategy for a God Class:
(ATFD > Few) AND (WMC >= Very High) AND (TCC < One Third)
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 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.
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.
