Code smells, slice-based
metrics and plenty of
deodorant
Steve Counsell
Brunel University
Alessandro Murgia
University of Cagliari
Introduction
Code smells are areas of code which
“scream out” to be refactored
 In theory, a code smell would be indicative
of a decaying class

 Methods
becoming longer
 Classes becoming bigger
 Coupling becoming greater
 Cohesion deteriorating
Commonly-studied smells (cont.)

Feature Envy
 “A
classic [code] smell is a method that seems more
interested in a class other than the one it is in. The
most common focus of the envy is the data” (Fowler)

God Class (aka Key Class)
A

Long Method
A

class that is deemed to have grown too large
method that is deemed to have grown too large
Long Parameter List
 Method
is deemed to have too many parameters
Commonly-studied smells (cont.)

Feature Envy
 Remedied
(deodorised) by ‘moving’ the method to the
class where it is most needed

God Class
 No


obvious remedy
Extract class?
Long Method
 Remedied

by splitting the method into at least two
Long Parameter List
What we did

Premise
 Smell-based

classes will have low cohesion
We used Eclipse
 Jdeodorant
 Allows smell extraction from Java systems

Slice-based metrics plug-in (Tsantalis et al.):
 Overlap
 Tightness
 Coverage
honestly!)
(omitted from this analysis for clarity –
Overlap and Tightness
(function F)
Overlap(F) =
1
| Vo |
| Vo |

i 1
Tightness(F) =
| SL int |
length ( F )
| SL int |
| SLi |
Program function
Main()
{
int i;
int smallest;
int largest;
int A[10];
SLsmallest
SLlargest
SLint
|
|
|
|
|
|
|
|
|
for (i=0; i <10; i++)
{
int num;
scanf(“%d”, &num);
A[i] = num;
}
|
|
|
|
|
|
|
|
|
|
|
|
smallest = A[0];
largest=smallest;
|
|
|
|
i=1;
while (i <10)
{
if (smallest > A[i])
smallest = A[i];
if (largest < A[i])
largest = A[i];
|
|
|
|
|
|
i = i +1;
}
|
printf(“%d \n”, smallest);
printf(”%d \n”, largest);
}
length =19
|
|
|
|
|
|
|
|
|
14
16
11
Overlap =
1
2
Tightness =
11
(
14
+
11
= 0.58
19
11
16
)
= 0.74
Study 1 – Evolizer
Evolizer tool

A tool for studying the evolution of OO systems
 Developed


at the University of Zurich
300 classes/interfaces
We looked at the following smells:
 God
class
 Long method
 Feature envy
God class
The JDeodorant tool found 18 occurrences
of the God class
 For each of the God classes

 We
extracted the two slice-based metrics for
all methods in those classes:
Overlap
 Tightness


Abstract classes/interfaces an issue
God class - problems
Constructors were often the largest
methods in a God class (metrics n/a)
 Single line methods with no local variables
were a frequent occurrence (metrics n/a)

 Get
and set methods
Single variable methods
 Use of ‘super’ to access a superclass

1.2
1
0.8
Overlap
0.6
Tightness
0.4
0.2
0
1
3
5
7
9 11 13 15 17 19 21 23 25 27 Low cohesion
Method
values are not
apparent
Tightness and Overlap (Counsell et
al 2010)
N
Mean
Max
SD
Median
Tightness
(fault-prone)
372
0.32
0.99
0.32
0.21
Tightness
(fault-free)
150
0.38
1.00
0.37
0.28
Overlap
(fault-prone)
372
0.59
1.00
0.33
0.63
Overlap
(fault-free)
150
0.63
1.00
0.38
0.72
Long method
The JDeodorant tool found 9 occurrences
of Long Method
 For each of these methods:

 We
extracted the same two slice-based
metrics for all methods in those classes
Overlap
 Tightness

Long method - problems
Constructors were often the largest
methods (metrics n/a)
 Single line methods with no local variables
were a frequent occurrence (metrics n/a)

 Get
and set methods
Single variable methods
 Use of ‘super’

1.2
1
0.8
Overlap
0.6
Tightness
0.4
0.2
0
1
2
3
4
5
Method
6
7
8
9
Low cohesion
Values not apparent
Feature envy
The JDeodorant tool found 11 occurrences
of Feature envy
 For each of these methods:

 We
extracted the two slice-based metrics for
all methods in those classes
Feature envy - problems

………same problems as with the other
two smells
1.2
1
0.8
Overlap
0.6
Tightness
0.4
0.2
0
1
2
3
4
5
6
Method
7
8
9
10
11
A hypothesis

In terms of cohesion, we would expect:
 Long
Method to contain the most un-cohesive
methods
 God class to contain the next most uncohesive methods
 Feature envy to be the most cohesive
A hypothesis – result mean
values

For Overlap:
 God
class most cohesive
 Feature envy
 Long method least cohesive

For Tightness:
 Long
method most cohesive
 Feature envy
 God class least cohesive

Most values of Overlap and Tightness >
0.5
Study 2 – Proprietary
System
Background to Study 2


C# sub-system for a web-based, loans system
providing quotes and financial information for
on-line customers
We examined two versions of one of its subsystems:
 an early version, comprised 401 classes
 later version (version n) had been the subject
of a
significant refactoring effort to amalgamate, minimize
as well as optimize classes

Comprised 101 classes only
Smell analysis

We focused on three smells which, arguably,
should be easily identifiable from the source
code:
 God
Class
 Long Method
 Lazy Class. A class is not doing enough to justify its
existence, identified by a small number of methods
and/or executable statements; it should be merged
with the nearest, related class
God Class

We found many god classes to be architectural
pattern-based class (Page Controller and Data
Transfer Objects)
 Should
be left alone, irrespective of the cohesion
value
 They also had relatively large amounts of coupling

So eradicating this smell would not only be
unwarranted, but difficult (because of the
coupling)
Long Method

Class ComparisonEngine.cs contained the method with
the highest number of statements.



Often ‘long’ methods are a necessary part of the
implementation of an architectural pattern


Inspection of the code revealed this method to contain one large
switch statement comprising 340 statements
Deodorising this method would be a major undertaking
Leave them alone
Found evidence of these features in both start and end
versions
Conclusions
Many problems with extracting the Weiser
set of metrics and interpretation in OO
 Use of parameters might be a better bet

 Use
of variables in any cohesion metric is
subject to various problems

Decision on eradication of smells
(deodorant) is a problem
 Might
explain the difficulty of capturing
cohesion
 Thanks
for listening!