Concern-Based Cohesion as Change Proneness Indicator: An Initial Empirical Study

Software Engineering Lab – UFBA

Salvador-Bahia-Brazil - http://les.dcc.ufba.br

Concern-Based Cohesion as

Change Proneness Indicator:

An Initial Empirical Study

1

Bruno C. da [email protected]

Cláudio Sant’[email protected]

Christina [email protected]

Federal

University of

Bahia

(UFBA)



RQ: Is cohesion useful for prediction of

module change proneness?

High cohesive few changes?

Low cohesive several changes?

Structure-based cohesion metrics

For instance, Lack of Cohesion in Methods (LCOM)

Fail to capture the semantic notion of module’s

cohesion

And may not be a good indicator of change

proneness

2



LCC (Lack of Concern-based

Cohesion)

An alternative cohesion metric

Centered on counting the number of concerns a module

implements

May capture better the semantic notion of module’s cohesion

It is part of a growing body of relevant work focusing on concern-

driven measurement

3

Concerns

Code

concern 1 concern 2 concern 3



This Work

An initial empirical assessment of the

correlation between LCC and change

proneness

Secondary goal:

LCOM vs. change proneness

LCC vs. efferent coupling

LCOM vs. efferent coupling

4



Empirical Study

5

V1 V2 V3…

Vn

Classes

Version HistoryLCC

LCOM

CBO

… … … …

# of versions each

class changed (NCh)



Empirical Study

6

Two target systems:Mobile Media (MM) – 6 versions

Health Watcher (HW) – 8 versions

and

Spearman correlation method for

measurements of both systems



Empirical Study – Correlation Tests

Cohesion vs. Changes

Concern-based cohesion vs. Changes (LCC vs. NCh)

7

NCh LCOM CBO LCC

Correlation

Coefficient

1 .564*

.618** .295

Sig. (2-tailed) . .012 .001 .162

Correlation

Coefficient.564

* 1 .488* .154

Sig. (2-tailed) .012 . .034 .530

Correlation

Coefficient.618

**.488

* 1 .504*

Sig. (2-tailed) .001 .034 . .012

Correlation

Coefficient

.295 .154 .504* 1

Sig. (2-tailed) .162 .530 .012 .

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

NCh

LCOM

CBO

LCC

NCh LCOM CBO LCC

Correlation

Coefficient

1 .086 .646**

,518**

Sig. (2-tailed) . .584 .000 .000

Correlation

Coefficient

.086 1 -,264 -,307*

Sig. (2-tailed) .584 . .087 .045

Correlation

Coefficient.646

** -,264 1 .694**

Sig. (2-tailed) .000 .087 . .000

Correlation

Coefficient.518

**-,307

*.694

** 1

Sig. (2-tailed) .000 .045 .000 .



NCh

LCOM

CBO

LCC

Mobile Media Health Watcher

No significance

levelStrong correlation




Cohesion vs. Changes

Structure-based cohesion vs. Changes (LCOM vs. NCh)

8

NCh LCOM CBO LCC

Correlation

Coefficient

1 .564*

.618** .295

Sig. (2-tailed) . .012 .001 .162

Correlation

Coefficient.564

* 1 .488* .154

Sig. (2-tailed) .012 . .034 .530

Correlation

Coefficient.618

**.488

* 1 .504*

Sig. (2-tailed) .001 .034 . .012

Correlation

Coefficient

.295 .154 .504* 1

Sig. (2-tailed) .162 .530 .012 .



NCh

LCOM

CBO

LCC

NCh LCOM CBO LCC

Correlation

Coefficient

1 .086 .646**

,518**

Sig. (2-tailed) . .584 .000 .000

Correlation

Coefficient

.086 1 -,264 -,307*

Sig. (2-tailed) .584 . .087 .045

Correlation

Coefficient.646

** -,264 1 .694**

Sig. (2-tailed) .000 .087 . .000

Correlation

Coefficient.518

**-,307

*.694

** 1

Sig. (2-tailed) .000 .045 .000 .



NCh

LCOM

CBO

LCC


Strong correlation No significance

level




Cohesion vs. Coupling

Concern-based cohesion vs. Coupling (LCC vs. CBO)

9

NCh LCOM CBO LCC

Correlation

Coefficient

1 .564*

.618** .295

Sig. (2-tailed) . .012 .001 .162

Correlation

Coefficient.564

* 1 .488* .154

Sig. (2-tailed) .012 . .034 .530

Correlation

Coefficient.618

**.488

* 1 .504*

Sig. (2-tailed) .001 .034 . .012

Correlation

Coefficient

.295 .154 .504* 1

Sig. (2-tailed) .162 .530 .012 .



NCh

LCOM

CBO

LCC

NCh LCOM CBO LCC

Correlation

Coefficient

1 .086 .646**

,518**

Sig. (2-tailed) . .584 .000 .000

Correlation

Coefficient

.086 1 -,264 -,307*

Sig. (2-tailed) .584 . .087 .045

Correlation

Coefficient.646

** -,264 1 .694**

Sig. (2-tailed) .000 .087 . .000

Correlation

Coefficient.518

**-,307

*.694

** 1

Sig. (2-tailed) .000 .045 .000 .



NCh

LCOM

CBO

LCC


Strong correlation Strong correlation




Cohesion vs. Coupling

Structure-based cohesion vs. Coupling (LCOM vs. CBO)

10

NCh LCOM CBO LCC

Correlation

Coefficient

1 .564*

.618** .295

Sig. (2-tailed) . .012 .001 .162

Correlation

Coefficient.564

* 1 .488* .154

Sig. (2-tailed) .012 . .034 .530

Correlation

Coefficient.618

**.488

* 1 .504*

Sig. (2-tailed) .001 .034 . .012

Correlation

Coefficient

.295 .154 .504* 1

Sig. (2-tailed) .162 .530 .012 .



NCh

LCOM

CBO

LCC

NCh LCOM CBO LCC

Correlation

Coefficient

1 .086 .646**

,518**

Sig. (2-tailed) . .584 .000 .000

Correlation

Coefficient

.086 1 -,264 -,307*

Sig. (2-tailed) .584 . .087 .045

Correlation

Coefficient.646

** -,264 1 .694**

Sig. (2-tailed) .000 .087 . .000

Correlation

Coefficient.518

**-,307

*.694

** 1

Sig. (2-tailed) .000 .045 .000 .



NCh

LCOM

CBO

LCC


Moderate correlation No significance

level



Empirical Study - Discussion

Low LCC and several changes

Fake Low-LCC classes (due to concerns which were

not mapped)

Correlation was affected by the quality of

concern-to-code mapping

Classes partially mapped

Existence of unmapped or “unknown” concerns

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Final Remarks

The study was constrained by:

the quality of the concern-to-code mapping

the small size of the involved systems

the small number of considered versions

Future work:

(Semi) automated techniques for concern-to-code mapping

Replicate the study with larger systems with a higher number of

releases

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Although the constraints, we consider that our findings

showed that LCC is worth to be further investigated






13




Thank you!






14




Backup slides


Salvador-Bahia-Brazil - http://les.dcc.ufba.br 15

The PhotoViewScreen class in the Mobile

Media System

V1 V2 V3 V4 V5 V6

LCOM = 0 However, it revealed to be a change prone class

Changes over 3 versions, totalizing 8 operation-related changes

and 4 field-related changes

Changes Changes Changes


Salvador-Bahia-Brazil - http://les.dcc.ufba.br 16

V1 V2 V3 V4 V5 V6

LCOM = 0

LCC = 4 (in a total of 5

mapped concerns in the system)

LCC as a better indicator of change proneness

Changes over 3 versions, totalizing 8 operation-related changes

and 4 field-related changes

Changes Changes Changes

The PhotoViewScreen class in the Mobile

Media System




High LCC and no changes

The case of 6 classes for exception handling in MM

For instance: ImageNotFoundException and

InvalidPhotoAlbumNameException

LCC = 3 (in a total of 5) and no changes from V1 to V6

Concerns: Persistence, Exception Handling and Photo

Reasons:

There was no change directly related to Persistence and

Exception Handling concerns

In V6, they should have been changed. But programmers forgot

or decided not to do that.

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Low LCC and several changes

The case of PhotoListScreen class in MM

Only 1 concern mapped to this class in V1

LCC = 1 (Photo concern)

However, this class was modified in thee versions (V2, V3

and V6),

Totalizing 6 operation-related changes and 8 field-related

changes

Changes occurred by different concerns which were not

mapped to this class (e.g. the Screen concern)

This class should have had a higher LCC value

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Imprecise concern-to-code mapping

Classes with code fragments not assigned to any of

the considered concerns

Or classes with LCC = 0 (no concerns mapped to

them), but they changed due to modifications by

unmapped or “unknown” concerns

A real problem

Time-consuming, subjective and error-prone activity

It lacks tool-support for a precise (semi) automatically

mapping

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Negative correlation between LCC and LCOM

HW system: negative moderate correlation

(coefficient -.307)

Reasons:

Classes with high LCC and low LCOM

The motivating example showed at the beginning

And others, such as the HealthWactherFacade class

LCC = 4 (Top LCC in HW version 1) and LCOM = 0

Classes with low LCC and high LCOM

FoodComplaint class (LCOM = 78 and LCC = 0)

Low LCC due to the problem of imprecise concern-to-code

mapping

High LCOM due to many fields and “getters” and “setters”

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Metrics for Changes

NCh (Number of Changes)

Counts the number of versions each class has

changed

Others:

NCh-Oper (Number of operation-related changes)

NCh-Field (Number of field-related changes)

We started using them, but they were discarded

The effect of using them was pretty much the same in our

study

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Concern-Based Cohesion as Change Proneness Indicator: An Initial Empirical Study

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