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

Concern-based Cohesion: Unveiling
a Hidden Dimension of Cohesion
Measurement
Bruno C. da Silva Cláudio Sant’Anna Christina Chavez Alessandro Garcia
brunocs@dcc.ufba.br santanna@dcc.ufba.br flach@dcc.ufba.br afgarcia@inf.puc-
afgarcia@inf.puc-rio.br

Federal University
of Bahia
(UFBA)

Software Design and Evolution Group
Software Engineering Lab – UFBA aside.dcc.ufba.br
Salvador-Bahia-Brazil - les.dcc.ufba.br

Cohesion can be defined as:

The degree to which a module
represents an abstraction of a
single concern of the software

Software Engineering Lab – UFBA
Salvador-Bahia-Brazil - les.dcc.ufba.br 2

Structural Cohesion Metrics

E.g. LCOM, LCOM2, etc.

Almost all methods share
the same instance
variable

Is it a high cohesive
class?

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

Lack of Concern-Based Cohesion (LCbC)

How many
concerns does this
class address?
http
response
LCbC = 6 buffer

http
response
header
Is it a high
cohesive class? URL enconding

web cookies
http redirecting
Software Engineering Lab – sending
Error UFBA
and others…
Salvador-Bahia-Brazil - les.dcc.ufba.br … 4

Cohesion: Structure-based vs. Concern-based

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

low lack of cohesion
LCOM2 = 0 or
high cohesion
Example – ResponseFacade (Tomcat)

high lack of cohesion
LCbC = 6 or
low cohesion


Empirical Study – First Goal

Provide empirical evidence about
whether the concern-driven nature of a
cohesion metric makes it significantly
different from structural cohesion
metrics.


Moreover…
http response
buffer
changes
http response
header

URL enconding

web cookies

http redirecting

Error sending …

… the number of concerns a module realizes may influence
positively the number of changes it may be subject to.

Empirical Study – Second Goal

Investigate whether and how concern-
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?


Research Questions

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


Research Questions

RQ3: Does the LCbC metric applied
together with structural cohesion
metrics enhance the prediction of
module changes?


Empirical Study Settings
Change history
Module1 - - - - -
Module 2 - - - -

System Module 3 - - - - - - -

…
Module n - - - -

LCOM2, LCOM3, Change Count (CC)
LCOM4, LCOM5,
TCC, LCbC


Change history
Module1 - - - - -
Module 2 - - - -

System Module 3 - - - - - - -
…
Module n - - - -

LCOM2, LCOM3, Change Count (CC)
LCOM4, LCOM5,
TCC, LCbC
System Revisions analyzed
JFreeChart 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-to-code mapping procedure

System
JFreeChart
Concerns automatically Freecol
mapped using the XScan tool jEdit
Tomcat
Findbugs
Manual concern mapping
Rhino
provided by Eaddy et al (2008)


RQ1: Does LCbC capture a dimension of module
cohesion that is not captured by structural cohesion
metrics?
Principal Component Analysis (PCA)

JFreeChart Rhino jEdit Tomcat Findbugs Freecol

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
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

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

Spearman Correlation: each cohesion metric vs CC
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
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
In jEdit and Findbugs LCbC did not perform well



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
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 and LCOM2 were the most correlated with
change count



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
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.



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
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).

RQ3: Does the LCbC metric applied together with
structural cohesion metrics enhance the prediction of
module changes?
Linear Regression Analysis
Metrics in the Final Model with Standardized Coefficients R2 (adj)
JFreeChart (0.47)LCOM2 + (0.11)LCOM3 + (0.59)LCbC + (-0.27)LCOM4 0.63
Rhino (0.63)LCOM2 + (0.37)LCOM3 + (0.18*)TCC 0.59
Findbugs (0.45)LCOM2 + (0.20)LCOM3 + (0.17)LCOM4 0.37
Freecol (0.44)LCOM2 + (0.21)LCOM3 + (0.11)LCbC 0.35
Tomcat (0.39)LCOM2 + (0.16)LCOM3 + (0.29)LCbC + (-0.07*)LCOM4 0.32
jEdit (0.20)LCOM2 + (0.35)LCOM4 + (0.09*)LCOM5 + (0.17)LCbC 0.26

LCbC ended up in four regression models


RQ3: Does the LCbC metric applied together with
structural cohesion metrics enhance the prediction of
module changes?
Linear Regression Analysis
Metrics in the Final Model with Standardized Coefficients R2 (adj)
JFreeChart (0.47)LCOM2 + (0.11)LCOM3 + (0.59)LCbC + (-0.27)LCOM4 0.63
Rhino (0.63)LCOM2 + (0.37)LCOM3 + (0.18*)TCC 0.59
Findbugs (0.45)LCOM2 + (0.20)LCOM3 + (0.17)LCOM4 0.37
Freecol (0.44)LCOM2 + (0.21)LCOM3 + (0.11)LCbC 0.35
Tomcat (0.39)LCOM2 + (0.16)LCOM3 + (0.29)LCbC + (-0.07*)LCOM4 0.32
jEdit (0.20)LCOM2 + (0.35)LCOM4 + (0.09*)LCOM5 + (0.17)LCbC 0.26

LCbC was the most important metric for the JFreeChart
regression model

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.




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


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.

Concern-based cohesion has provided indications that
it is worth to be further investigated.


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
interesting factor to be considered

The application of different regression analysis
techniques

Search for more complete concern mappings


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

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