COMPSAC 2014

Empirically Based Evolution of a

Variability Management Approach

at UML Class Level

Anderson Marcolino, M.Sc.State University of Maringá (DIN-UEM) – Brazil

Edson OliveiraJr, [email protected]

State University of Maringá (DIN-UEM) - Brazil

Itana Gimenes, Ph.D.State University of Maringá (DIN-UEM) – Brazil

Ellen Barbosa, Ph.D.University of São Paulo (ICMC-USP) – Brazil

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Agenda

Introduction

Background

SPL and Variability Management

The Gomaa Method

The SMarty Approach

SMarty Evolution based on Empirical Study

Conclusion and Future Works

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Introduction Software Product Line (SPL) is a consolidated reuse technique,

in which several products share similar features and variabilities

allowing mass customization for specific market needs

Variability represents how such products differ one another, thus

Variability Management (VM) is a key issue for the success of SPLs

Literature presents several well-known approaches for VM,

especially object-oriented and UML-based.

However, their effectiveness was not experimentally analyzed, which can

make technology transfer feasible

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Introduction

The SPL approach encompasses three main

activities:

Domain Engineering

Application Engineering

Management (Organizational and Technical)

Variability management is one of the most important

SPL management activities

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Introduction Four main concepts are taken into consideration

for VM: Variability, Variation Point and Variants

PLUS and SMarty are two well-known

approaches for VM in UML-based SPLs However, PLUS has been taken as a basis for several UML-

based SPL projects

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Objectives of this work

1. Comparing the effectiveness of SMarty and

PLUS with regard to the identification and

representation of variability in UML classes

2. Allowing the SMarty evolution in order to

increase its effectiveness against PLUS

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Background - PLUS The PLUS method

It encompasses a variability management activity with regard to

use cases and classes, using stereotypes with no guidelines to

apply them / Domain experts must use their knowledge for VM /

Variant constraints are not available.

Use cases only!!!

Use cases only!!!

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Background - PLUS An example of the PLUS variability representation for

classes:

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Background - SMarty

Stereotype-based Management of Variability

(SMarty) approach

It is composed of:

an UML 2 profile, the SMartyProfile with stereotypes

for VM in use case, class, component, sequence, and

activity diagrams; and

a process, the SMartyProcess, with guidelines to

support variability identification and representation

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Background - SMarty SMartyProfile comprises the following stereotypes, which

can be applied to UML models:

<<variability>> represents the concept of PL variability;

<<variant>> this abstract stereotype is specialized in four other non-

abstract stereotypes which are: <<mandatory>>, <<optional>>,

<<alternative_OR>>, and <<alternative_XOR>>;

<<mutex>> is a mutually exclusive relationship between two variants;

and

<<requires>> is a relationship between two variants in which the

selected variant requires the presence of another specific variant.

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Background - SMarty An example of the

SMarty variability

representation for

classes:

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The Experimental Study

Aim (Basili’s GQM template)

Compare PLUS and SMarty, for the purpose of characterize

the most effectiveness, with respect to the capability of

identification and representation of variabilities in Software

Product Line class models, from the point of view of software

product line architects, in the context of master and Ph.D.

students of the Software Engineering area from the University of

São Paulo (ICMC/USP) and Federal University of São Carlos

(UFSCar).

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Planning

Pilot Project

Selection of Subjects

Training

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Planning

Hypotheses Formulation

H0 : µ (effectiveness(X)) = µ (effectiveness(Y));

H1 : µ (effectiveness(X)) < µ (effectiveness(Y)); and

H2 : µ (effectiveness(X)) > µ (effectiveness(Y)).

Where X = PLUS and Y = SMarty.

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The Experimental Study Planning

Dependent Variables: the effectiveness calculated for each

variability management approach (X and Y) as follows:

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Planning

Independent Variables:

the variability management approach, a factor with

two treatments (X and Y); and

the SPL, a factor with two treatments (E-commerce

and AGM SPLs).

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Execution

Instrumentation

Participation Procedure

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Effectiveness of the Approaches

Collected Data Normality Test: the Shapiro-Wilk normality test

was applied to the E-commerce and AGM samples providing the

following results:

sample X was considered non-normal and

sample Y was considered normal.

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Effectiveness of the Approaches

Mann-Whitney-Wilcoxon for SampleX and SampleY:

Detailed explanation on samples is in the paper

There is evidence that the X approach (PLUS) is more effective in

identifying and representing variability in class models than the Y

approach (SMarty)

This result corroborates to reject the null hypothesis (H0)

of this study: PLUS is more effective than SMarty for

SPLs class models and subjects taken into account.

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Threats to Validity

Conclusion Validity

Sample size is a major concern, which must be increased in

prospective studies

Random capacity was not applied to subjects selection, thus

generalization of results could not be inferred

Construct Validity

Independent variable variability modeling approach was

guaranteed by the pilot project undertaken

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Threats to Validity

Internal Validity

Differences among subjects – variations on skills were reduced by

performing training session and tasks in the same order

Fatigue effects – on average the experiment took 80 minutes, thus

fatigue was not considered a problem for the study

Influence among subjects – could not be really controlled. They took

the experiment under the supervision of a human observer.

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Threats to Validity

External Validity

Instrumentation – failing to use real class models, as the e-commerce

and AGM are not commercial SPLs. More experiments must be

carried out with real SPLs.

Subjects – we could take advantages of the benefits of taking into

account students for performing experiments as pointed out by

Carver et al. (2003)

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Empirical Evolution of SMarty

Improvements for the SMarty approach based on the

feedback of the subjects:

E-commerce SPL

Subjects reported difficulties on the application of SMarty to the e-

commerce SPL class model as it has no elements of class modeling,

such as, inheritance, aggregation, and generalization E-

commerce was analyzed to improve SMarty

Training session

Subjects indicated that they need more time for training and

application of SMarty

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Improvements for the SMarty approach based on the

feedback of the subjects:

Amount of stereotypes

Subjects questioned the difference between PLUS and SMarty as

the later has several more stereotypes

Arrangement of the SPL models:

E-commerce class models are mode complex than AGM

E-commerce models were the first models given to the subjects

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Based on such subjects feedback, the following

improvements were made:

New guidelines were added to SMarty, encompassing the level of

abstraction and elements of the e-commerce class models CL2. Class models elements, related with associations in which their

attributes have aggregationKind as none, or, do not represent aggregation or composition, suggest mandatory or optional variants.

CL2.1. For the identification of possible optional variants classes related through associations where the multiplicity in one of the ends of an association, to each object class found at the opposite end match * (zero or more) or 0..n where n is any integer different from zero; suggest an optional class

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Based on such subjects feedback, the following

improvements were made:

We realized that with a few less stereotypes, PLUS is easier to

apply. However, such an “easibility” might jeopardize the

generation of specific products as we can observe ambiguity in

PLUS class models

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Revisiting objectives of this work…

1. Comparing the effectiveness of SMarty and PLUS with

regard to the identification and representation of

variability in UML classes

PLUS is more effective

2. Allowing the SMarty evolution in order to increase its

effectiveness against PLUS

New guidelines for class models need a new experiment

Reduce stereotypes or avoid ambiguity during specific products

generation? New experiment is being carried out

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Conclusion Industry needs that the scientific community tests existing and new

technologies, such as SMarty, identifying their effectiveness to make

technology transfer easier and reliable.

The experimental study presented in this paper demonstrates the

ability to use variability management approaches. Their

effectiveness were analyzed by modeling variability in class models

of two SPLs.

Empirical based improvements could be made to SMarty in order to

incorporate support for different class model elements by means of

new guidelines

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Conclusion Shapiro-Wilk normality test was applied to both samples

Mann-Whitney-Wilcoxon test analyzed the effectiveness of PLUS

and SMarty

Obtained results provide evidence that the PLUS is more effective

than SMarty for modeling variability in UML class models, taking into

account the E-commerce and the AGM SPLs for this study.

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Future Works

New experimental studies and replications must be planned and

conducted to make it possible to reduce the threats, increasing the

effectiveness of SMarty towards generalizing the results.

As new experiments, we are:

planning a new experiment for characterize the improved version of

SMarty effective against PLUS; and

planning an experiment to analyze the effectiveness of PLUS and

SMarty and their impact on the generation of specific products.

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Acknowledgements

The authors would like to thank

CAPES (a Brazilian Funding Agency)

for supporting this work!!!

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Questions? Contributions?

If you prefer, contact me via e-mail:

[email protected]

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Tack! ! !

Thank you ! ! !

Education

COMPSAC 2014