Simple Oriented Architecture

Clean Code

How to identify a Tradition Breaker using NDepend

In the previous articles in this series we’ve seen:

In this article we’ll see how to identify the Tradition Breaker code smell.

Tradition Breaker Detection Strategy

A class suffers from Tradition Breaker when it doesn’t use the protected members of its parent. Object-Oriented Metrics in Practice, by Michele Lanza and Radu Marinescu, proposes the following detection strategy for Tradition Breaker:

((NAS >= Average NOM per class) AND (PNAS >= Two Thirds)) AND
(((AMW > Average) OR (WMC >= Very High)) AND (NOM >= High)) AND
((Parent’s AMW > Average) AND (Parent’s NOM > High/2) AND (Parent’s WMC >= Very High/2))

This might seem complex on a first look. After we go over the definition for each metric, we’ll break this detection strategy in three distinct parts. This way we’ll see why the authors picked these conditions and it will make more sense.

Clean Code

How to identify Refused Parent Bequest using NDepend

In the previous articles in this series we’ve seen:

In this article we’ll see how to identify the Refused Parent Bequest code smell.

Refused Parent Bequest Detection Strategy

A class suffers from Refused Parent Bequest when it doesn’t use the protected members of its parent. Object-Oriented Metrics in Practice, by Michele Lanza and Radu Marinescu, proposes the following detection strategy for Refused Parent Bequest:

(((NProtM > Few) AND (BUR < A Third)) OR (BOvR < A Third)) AND
(((AMW > AVerage) OR (WMC > Average)) AND (NOM > Average))

Clean Code

How to identify Shotgun Surgery using NDepend

In the previous articles in this series we’ve seen:

In this article we’ll see how to identify an afferent (incoming) coupling code smell: Shotgun Surgery.

Shotgun Surgery Detection Strategy

A method suffers from Shotgun Surgery if it is called many times from many other classes. Object-Oriented Metrics in Practice, by Michele Lanza and Radu Marinescu, proposes the following detection strategy for Shotgun Surgery:

(CM > Short Memory Cap) AND (CC > Many)

Clean Code

How to identify efferent coupling code smells using NDepend

In the previous articles in this series we’ve seen:

In this article we’ll see how to identify two types of efferent (outgoing) coupling code smells: Intensive Coupling and Dispersed Coupling.

Detection Strategies

Intensive Coupling

A method suffers from Intensive Coupling when it calls many other methods from a few classes. Object-Oriented Metrics in Practice, by Michele Lanza and Radu Marinescu, proposes the following detection strategy for Intensive Coupling:

(((CINT > Short Memory Cap) AND (CDISP < Half)) OR
((CINT > Few) AND (CDISP < A Quarter))) AND
(MAXNESTING > Shallow)

Dispersed Coupling

A method suffers from Dispersed Coupling when it calls many other methods that are dispersed among many classes. The detection strategy for Dispersed Coupling is:

(CINT > Short Memory Cap) AND (CDISP >= Half) AND (MAXNESTING > Shallow)

Clean Code

How to identify a Brain Method using NDepend

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 Method Detection Strategy

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)

Clean Code

How to identify a Data Class using NDepend

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 Class Detection Strategy

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)))

Clean Code

How to identify Feature Envy using NDepend

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)

Clean Code

How to identify a God Class using NDepend

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.

God Class Detection Strategy

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)

Quality

Computing the Overview Pyramid metrics with NDepend

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 — source: https://commons.wikimedia.org/wiki/File:Overview_pyramid.jpg

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.

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.