Concern-based Cohesion: Unveiling a Hidden Dimension of Cohesion Measurement

Concern-based Cohesion: Unveiling

a Hidden Dimension of Cohesion

Measurement

Software Engineering Lab – UFBA

Salvador-Bahia-Brazil - les.dcc.ufba.br

Software Design and Evolution Group

aside.dcc.ufba.br

Measurement

Bruno C. da SilvaBruno C. da [email protected]@dcc.ufba.br

Cláudio Cláudio Sant’AnnaSant’[email protected]@dcc.ufba.br

Christina Christina [email protected]@dcc.ufba.br

Federal University of Bahia (UFBA)

Alessandro GarciaAlessandro [email protected]@inf.puc--rio.brrio.br

Cohesion can be defined as:

The degree to which a module represents an abstraction of a



represents an abstraction of a single concern of the software

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Structural Cohesion Metrics

Almost all methods share

the same instance

E.g. LCOM, LCOM2, etc.


Salvador-Bahia-Brazil - les.dcc.ufba.br 3…

the same instance

variable

Is it a high cohesive

class?

Lack of Concern-Based Cohesion (LCbC)

How many

concerns does this

class address?

http

response


Salvador-Bahia-Brazil - les.dcc.ufba.br 4

LCbC = 6

…

http

response

header

response

buffer

URL enconding

web cookies

Error sending and others…

http redirecting

Is it a high

cohesive class?

Cohesion: Structure-based vs. Concern-based

They capture different dimensions of cohesion• Different source of information and counting

mechanism;

• Different interpretation of cohesion;



LCOM2 = 0

LCbC = 6

Example – ResponseFacade (Tomcat)

low lack of cohesion

or

high cohesion

high lack of cohesion

or

low cohesion

Empirical Study – First Goal

Provide empirical evidence about

whether the concern-driven nature of a

cohesion metric makes it significantly



cohesion metric makes it significantly

different from structural cohesion

metrics.

Moreover…

http response

buffer

http response

header

URL enconding

changes



… the number of concerns a module realizes may influence

positively the number of changes it may be subject to.

…

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web cookies

http redirecting

Error sending

Empirical Study – Second Goal

Investigate whether and how concern-

based cohesion is associated to



based cohesion is associated to

change-proneness.

Research Questions

RQ1: Does LCbC capture a dimension of module cohesion that is not captured by structural cohesion metrics?



by structural cohesion metrics?

9

Research Questions

RQ2: How strong is the correlation between LCbC and module change-proneness?



proneness?

10

Research Questions

RQ3: Does the LCbC metric applied together with structural cohesion



together with structural cohesion metrics enhance the prediction of module changes?

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Empirical Study Settings

Change history

Module1 - - - - -

Module 2 - - - -

Module 3 - - - - - - -

…

Module n - - - -

System



Module n - - - -

LCOM2, LCOM3,

LCOM4, LCOM5,

TCC, LCbC

Change Count (CC)

Change history

Module1 - - - - -

Module 2 - - - -

Module 3 - - - - - - -

…

Module n - - - -

System

LCOM2, LCOM3,

LCOM4, LCOM5,

TCC, LCbC

Change Count (CC)

System Revisions analyzed



System Revisions analyzedJFreeChart 2,272

Freecol 3,426

jEdit 2,916

Tomcat 3,157

Findbugs 3,765

Rhino 777

Total 16,313


LCbC needs a concern-to-code mapping

concern A concern B concern C


Salvador-Bahia-Brazil - les.dcc.ufba.br 14…

concern A concern B concern C

…

SystemJFreeChart

Freecol

jEdit

Concerns automatically

mapped using the XScan tool


Concern-to-code mapping procedure



jEdit

Tomcat

Findbugs

Rhino

mapped using the XScan tool

Manual concern mapping

provided by Eaddy et al (2008)

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PC1 PC2 PC3 PC4 PC1 PC2 PC3 PC4 PC1 PC2 PC3 PC4 PC1 PC2 PC3 PC4 PC1 PC2 PC3 PC4 PC1 PC2 PC3 PC4

LCOM2 0.94 0.14 - 0.11 0.11 0.96 0.04 0.11 0.07 0.06 0.98 0.08 0.04 0.09 0.08 0.25 0.96 0.12 0.04 0.14 0.98 - 0.12 0.72 0.41 0.34

FreecolJFreeChart Rhino jEdit Tomcat Findbugs

RQ1: Does LCbC capture a dimension of module

cohesion that is not captured by structural cohesion

metrics?

Principal Component Analysis (PCA)



LCOM2 0.94 0.14 - 0.11 0.11 0.96 0.04 0.11 0.07 0.06 0.98 0.08 0.04 0.09 0.08 0.25 0.96 0.12 0.04 0.14 0.98 - 0.12 0.72 0.41 0.34

LCOM3 0.02 0.72 - 0.43 0.37 0.23 0.72 0.43 0.24 0.90 0.12 0.17 0.07 0.89 0.15 0.12 0.08 0.90 0.12 0.10 0.12 0.64 0.20 0.53 0.15

LCOM4 0.87 0.03 - 0.04 0.37 0.94 0.19 0.07 0.09 0.18 0.09 0.97 0.08 0.11 0.09 0.95 0.25 0.12 0.00 0.98 0.14 0.16 0.09 0.03 0.96

LCOM5 0.14 0.94 - 0.12 - 0.04 0.11 0.21 0.94 0.19 0.87 0.12 - 0.03 0.06 0.88 0.04 - 0.06 0.13 0.88 0.06 - 0.02 0.07 0.27 - 0.06 0.89 0.01

TCC - 0.12 - 0.24 0.95 - 0.09 - 0.08 - 0.95 - 0.08 - 0.02 - 0.79 0.12 - 0.21 0.06 - 0.85 - 0.01 - 0.16 0.03 - 0.80 0.06 - 0.14 - 0.04 - 0.89 - 0.05 - 0.16 - 0.11

LCbC 0.51 0.10 - 0.13 0.81 0.11 0.11 0.19 0.97 0.04 0.04 0.08 0.99 0.10 0.99 0.08 0.07 0.05 0.99 0.00 0.04 0.22 0.89 - 0.18 - 0.03

LCbC was the major metric of at least one PC in all systems. And in most of the systems it contributed exclusively for a PC

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RQ2: How strong is the correlation between LCbC and

module change-proneness?

Spearman Correlation: each cohesion metric vs CC

JFreeChart Rhino jEdit Tomcat Findbugs Freecol

LCOM2 0.48 0.69 0.16 0.33 0.48 0.49

LCOM3 0.34 0.48 0.17 0.27 0.38 0.19



In jEdit and Findbugs LCbC did not perform well

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LCOM3 0.34 0.48 0.17 0.27 0.38 0.19

LCOM4 0.32 0.46 0.10 0.21 0.23 0.20

LCOM5 0.15 0.30 0.18 0.23 0.34 0.22

TCC 0.24 0.22 0.13 0.16 0.06* 0.30

LCbC 0.66 0.62 0.15 0.35 0.21 0.46

* no signicance level





LCOM2 0.48 0.69 0.16 0.33 0.48 0.49

LCOM3 0.34 0.48 0.17 0.27 0.38 0.19



LCbC and LCOM2 were the most correlated with

change count

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LCOM3 0.34 0.48 0.17 0.27 0.38 0.19

LCOM4 0.32 0.46 0.10 0.21 0.23 0.20

LCOM5 0.15 0.30 0.18 0.23 0.34 0.22

TCC 0.24 0.22 0.13 0.16 0.06* 0.30

LCbC 0.66 0.62 0.15 0.35 0.21 0.46



LCOM2 0.48 0.69 0.16 0.33 0.48 0.49

LCOM3 0.34 0.48 0.17 0.27 0.38 0.19






LCOM3 0.34 0.48 0.17 0.27 0.38 0.19

LCOM4 0.32 0.46 0.10 0.21 0.23 0.20

LCOM5 0.15 0.30 0.18 0.23 0.34 0.22

TCC 0.24 0.22 0.13 0.16 0.06* 0.30

LCbC 0.66 0.62 0.15 0.35 0.21 0.46


In Rhino and Freecol, LCbC was the second most correlated (strong and moderate, respectively) preceded by LCOM2.

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LCOM2 0.48 0.69 0.16 0.33 0.48 0.49

LCOM3 0.34 0.48 0.17 0.27 0.38 0.19






LCOM3 0.34 0.48 0.17 0.27 0.38 0.19

LCOM4 0.32 0.46 0.10 0.21 0.23 0.20

LCOM5 0.15 0.30 0.18 0.23 0.34 0.22

TCC 0.24 0.22 0.13 0.16 0.06* 0.30

LCbC 0.66 0.62 0.15 0.35 0.21 0.46


LCbC was the most correlated with change count in JFreeChart (strong correlation) and Tomcat (moderate correlation).

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RQ3: Does the LCbC metric applied together with

structural cohesion metrics enhance the prediction of

module changes?

Linear Regression AnalysisR

2 (adj)

JFreeChart 0.63

Rhino 0.59

(0.47)LCOM2 + (0.11)LCOM3 + (0.59)LCbC + (-0.27)LCOM4

(0.63)LCOM2 + (0.37)LCOM3 + (0.18*)TCC

Metrics in the Final Model with Standardized Coefficients



LCbC ended up in four regression models

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Rhino 0.59

Findbugs 0.37

Freecol 0.35

Tomcat 0.32

jEdit 0.26


(0.20)LCOM2 + (0.35)LCOM4 + (0.09*)LCOM5 + (0.17)LCbC

(0.63)LCOM2 + (0.37)LCOM3 + (0.18*)TCC

(0.45)LCOM2 + (0.20)LCOM3 + (0.17)LCOM4

(0.44)LCOM2 + (0.21)LCOM3 + (0.11)LCbC

(0.39)LCOM2 + (0.16)LCOM3 + (0.29)LCbC + (-0.07*)LCOM4

RQ3: Does the LCbC metric applied together with

structural cohesion metrics enhance the prediction of

module changes?

Linear Regression AnalysisR

2 (adj)

JFreeChart 0.63

Rhino 0.59

(0.47)LCOM2 + (0.11)LCOM3 + (0.59)LCbC + (-0.27)LCOM4

(0.63)LCOM2 + (0.37)LCOM3 + (0.18*)TCC

Metrics in the Final Model with Standardized Coefficients



LCbC was the most important metric for the JFreeChartregression model

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Rhino 0.59

Findbugs 0.37

Freecol 0.35

Tomcat 0.32

jEdit 0.26


(0.20)LCOM2 + (0.35)LCOM4 + (0.09*)LCOM5 + (0.17)LCbC

(0.63)LCOM2 + (0.37)LCOM3 + (0.18*)TCC

(0.45)LCOM2 + (0.20)LCOM3 + (0.17)LCOM4

(0.44)LCOM2 + (0.21)LCOM3 + (0.11)LCbC

(0.39)LCOM2 + (0.16)LCOM3 + (0.29)LCbC + (-0.07*)LCOM4

Examples that illustrate the differences on

the dimensions of cohesion captured by

LCbC and structural cohesion metrics

Class (System) LCbC (Rank) LCOM2 (Rank) CC (Rank)

ResponseFacade (Tomcat) 10 (top 2%) 0 5 (top 20%)

CombinedRangeXYPlot (JFreeChart) 11 (top 5%) 33 (top 35%) 11 (top 10%)













Facade class usually has methods related to different

concerns because it serves as entrance point for

different functionalities.










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Concerns related to: drawing, zooming, axis space, click

handling and plotting.

When concern-based cohesion fails in the

association with changes


When the concern-to-code mapping fails to

identify concerns!




jEdit (jEdit) 0 9351 (3rd) 24 (2nd)

JEditBuffer (jEdit) 0 5913 (4th) 17 (5th)

SortedBugCollection (Findbugs) 0 1889 (5th) 76 (4th)

Threats to Validity

Quality of concern-to-code mapping

Underlying tool for concern mapping



Change Count

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Conclusions

LCbC defined itself a new and orthogonal dimension of module cohesion in the studied systems.

LCbC performed well in the association with change-proneness in most of the systems.



proneness in most of the systems.

Concern-based cohesion has provided indications that

it is worth to be further investigated.

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Future Work

�How LCbC performs in comparison with topic-based cohesion metrics such as C3 and MWE

�The association between LCbC and fault-proneness

�Whether or not the type of class would be an



�Whether or not the type of class would be an interesting factor to be considered

�The application of different regression analysis techniques

�Search for more complete concern mappings

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Concern-based Cohesion: Unveiling

a Hidden Dimension of Cohesion

Measurement



Software Design and Evolution Group

aside.dcc.ufba.br

Measurement

Bruno C. da SilvaBruno C. da [email protected]@dcc.ufba.br

Cláudio Cláudio Sant’AnnaSant’[email protected]@dcc.ufba.br

Christina Christina [email protected]@dcc.ufba.br

Federal University of Bahia (UFBA)

Alessandro GarciaAlessandro [email protected]@inf.puc--rio.brrio.br

Concern-based Cohesion: Unveiling a Hidden Dimension of Cohesion Measurement

Technology

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