1.
Understanding
Software Metrics
Tushar Sharma

2.
Tushar Sharma
Agenda
OO metrics
and their
classification
Using
metrics in
practice
Advanced
stuff…
Metrics:
What and
why

3.
Software Metrics - What & Why?
A software metric is a measure of some property of a
software system.
Why?
“Measurement is the first step that leads to control and
eventually to improvement. If you can’t measure
something, you can’t understand it.”
(by James Harrington)

4.
Tushar Sharma
Object-oriented Metrics
Size
Complexity
Cohesion &
Coupling
Project Namespace Type Method
LOC
NOC
NON
LOC
NOC
LOC
NOM
NOF
DIT
NC
RFC
LOC
NP
WMC CC
LCOM
CBO
Fan-in/
Fan-out

5.
Tushar Sharma
OO Metrics - Size
• Large entities make the comprehension difficult.
• Large entities indicate presence of various smells.
LOC Lines Of Code
NON Number Of Namespaces
NOC Number Of Classes
NOM Number Of Methods
NOF Number Of Fields

6.
Tushar Sharma
OO Metrics - Size
The response set of a class is a set
of methods that can potentially be
executed in response to a message
received by an object of that
class.
• Larger the number of methods that can be invoked, the greater the
complexity of the class.
• If a large number of methods can be invoked in response to a
message, the testing and debugging of the class becomes more
complicated .
RFC Response For Class

7.
Tushar Sharma
OO Metrics - Size (Hierarchy)
• Deep inheritance trees make
the comprehension difficult.
• The deeper a class is in the
hierarchy, the greater the
number of methods it is likely
to inherit, making it more
complex to predict its
behaviour.
• Again, wide inheritance trees make the comprehension
difficult.
• A wide hierarchy indicates missing intermediate inheritance
level that in turn may lead to code duplication.
DIT Depth of Inheritance Tree
NC Number of Children

8.
Tushar Sharma
OO Metrics - Cohesion and Coupling
Consider a class C with n methods M1, M2..., Mn. Let {Ij} = set of
instance variables used by method Mi.
There are n such sets I1 ,…, In
• P = {(Ii, Ij) | (Ii ∩ Ij ) = ∅}
• Q = {(Ii, Ij) | (Ii ∩ Ij ) ≠ ∅}
LCOM = |P| - |Q|, if |P| > |Q|
= 0, otherwise
• High LCOM indicate the violation of the Single Responsibility
Principle.
LCOM Lack of Cohesion
Of Methods

9.
Tushar Sharma
OO Metrics - Cohesion and Coupling
CBO for a class is a
count of the number
of other classes to
which it is coupled.
• Excessive coupling between object classes is detrimental to
modular design and prevents reuse. The more independent a
class is, the easier it is to reuse it in another application.
• A measure of coupling is useful to determine how complex the
testing of various parts of a design are likely to be. The higher
the inter-object class coupling, the more rigorous the testing
needs to be.
CBO Coupling Between Object classes

10.
Tushar Sharma
OO Metrics - Complexity
• CC - It is a quantitative measure of the number
of linearly independent paths through a
program's source code.
• WMC – Sum of CC of each method in the class
• The number of methods and the complexity of methods involved
is a predictor of how much time and effort is required to develop
and maintain the class.
CC Cyclomatic Complexity
WMC Weighted Methods per Class

11.
Tushar Sharma
Using metrics in practice - tools
C/C++
CppDepend
Infusion
Source Monitor
C#
Designite
NDepend
Java
Infusion
Source Monitor

12.
Tushar Sharma
Using metrics in practice
• There is no one single widely accepted standard for metrics
thresholds.
• There are some guidelines and rules of thumb!
Metric - Recommended Threshold
LOC (Method) - 50
LOC (File) - 500
NOC - 9
DIT - 5
LCOM - 0.80
NOM - 30
CBO - 6
CC - 10
WMC - 100
% Duplication - 4%
Metric thresholds

13.
Tushar Sharma
Using metrics in practice
What if you want to perform an analysis with customized metric
thresholds
http://www.designite-tools.com

14.
Tushar Sharma
What can be done with metrics data
• Indicator of “Structural Quality”
• Refactoring candidates
LackofCohesion
0
500
1000
1500
2000
2500
3000
3500
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Classes
Lackofcohesionvalue
0 120 180
Threshold
• Trend analysis
• Proof – Improved quality

15.
Tushar Sharma
What can be done with metrics data
• Detect code/design smells:
✦ Insufficient Modularization - WMC or NOM
✦ Hub-like Modularization – Fan-in/Fan-out
✦ Deep Hierarchy – DIT
✦ Multifaceted Abstraction – LCOM
✦ …

16.
Some more metrics
❖ Mood (Metrics for Object-Oriented Design)
✦ Method Hiding Factor (MHF)
✦ ratio of sum of the invisibilities of all methods defined in all
classes to the total number of methods
✦ Attribute Hiding Factor (AHF)
✦ Method Inheritance Factor (MIF)
✦ ratio of the sum of the inherited methods in all classes to the
total number of available methods (locally defined plus
inherited) for all classes
✦ Attribute Inheritance Factor (AIF)
✦ …

17.
Tushar Sharma
Abstractness Vs Instability
• Dependency
• say class Copy depends on KeyboardReader
• “Good dependency”?
• where target of the dependency is stable
• stable - does not depend on anything, not going to change
• Responsible class
• many classes depend on the class
• it’s harder to change
• and therefore, must be stable

18.
Tushar Sharma
• Instability
• Ca - Afferent coupling (Fan-in)
• Ce - Efferent coupling (Fan-out)
Abstractness Vs Instability
• So, shouldn’t we have all classes as stable classes?
• No. System will be unchangeable.
• Therefore, we need some portion of the design to be flexible to
withstand change.
• How? Abstract classes
• Abstractness = #abstract classes / #total classes in the group

19.
Tushar Sharma
Abstractness Vs Instability
(1, 0) - Max
instable and
concrete
(0, 0) - Max
stable and
concrete
(0.5, 0.5) -
striking the
balance
(0, 1) - Max
stable and
abstract
Zone of pain
Zone of uselessness
M
ain
sequence
Abstractness
Instability
(1, 1) - abstract
and max
instable (no
dependents)

20.
Tushar Sharma
Let’s conclude!!
Strive quality not numbers.

21.
Thank you
for your attention.
tusharsharma@ieee.org