Code smells, slice-based Code smells, slice-based metrics and plenty of metrics and plenty of

deodorantdeodorant

                  Steve Counsell Steve Counsell Brunel UniversityBrunel University

Alessandro Murgia Alessandro Murgia University of Cagliari 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 longerClasses becoming biggerCoupling 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 class that is deemed to have grown too large

Long Method A 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 (omitted from this analysis for clarity –

honestly!)

Overlap and Tightness (function F)

)(

|| int

Flength

SL

|Vo|

1

|Vo |

1i |SLi |

|| intSL

Tightness(F) =

Overlap(F) =

Program function SLsmallest SLlargest SLint Main() { int i; int smallest; int largest; int A[10]; 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

Tightness = 19

11 = 0.58

2

114

11

16

11Overlap = ( +

) = 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

0

0.2

0.4

0.6

0.8

1

1.2

1 3 5 7 9 11 13 15 17 19 21 23 25 27

Method

Overlap

Tightness

Low cohesionvalues 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’

0

0.2

0.4

0.6

0.8

1

1.2

1 2 3 4 5 6 7 8 9

Method

Overlap

Tightness

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

0

0.2

0.4

0.6

0.8

1

1.2

1 2 3 4 5 6 7 8 9 10 11

Method

Overlap

Tightness

A hypothesis

In terms of cohesion, we would expect: Long Method to contain the most un-cohesive

methodsGod class to contain the next most un-

cohesive methodsFeature envy to be the most cohesive

A hypothesis – result mean values For Overlap:

God class most cohesiveFeature envy Long method least cohesive

For Tightness:Long method most cohesiveFeature envyGod 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 sub-systems: 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. Inspection of the code revealed this method to contain one large

switch statement comprising 340 statements Deodorising this method would be a major undertaking

Often ‘long’ methods are a necessary part of the implementation of an architectural pattern 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 problemMight explain the difficulty of capturing cohesion

Thanks for listening!