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The term variability generally refers to the ability to change; to be more precise, this kind of
variability does not occur by chance but is brought about on purpose. In other words,
variability is a way to represent choices. Pohl et al. [20] suggest the three following
questions to define variability.
What does it vary?
: Identifying precisely the variable item or property of the real world.
The question leads us to the definition of the term variability subject (A variability subject is
a variable item of the real world or a variable property of such an item).
Why does it vary?
: There are different reasons for an item or property to vary: different
stakeholders' needs, different countries laws, technical reasons, etc. Moreover, in the case of
interdependent items, the reason for an item to vary can be the variation of another item
.
How does it vary?
: This question deals with the different shapes a variability subject can
take. To identify the different shapes of a variability subject, we define the term variability
object (a particular instance of a variability subject).
Example of variability
Subject and Objects for “Car”:
The variability subject “car” identifies a property of real-world items. Examples of
variability objects for this variability subject are Toyota, Nissan, and Proton.
The problem of representing variability in a DSS requires a complex representation scheme
to capture static and dynamic phenomena of the choices that can be encountered during the
decision process. We believe that the key feature of such knowledge representation (for
variability in a DSS) is its capability of precise representation of diverse types of choices and
associations within them. This involves: i) qualitative or quantitative description of choices
and their classification, ii) representation of causal relationships between choices and iii) the
possibility of computerizing the representation.
The main aim of variability representing in DSS is to create a decision repository that
contains decision points, its related choices and the constraint dependency relations
between decision points-choices, choices-choices, or decision points-decision points.
Nowadays, Feature Model (FM) [12] and Orthogonal Variability Model (OVM) [20] are the
well-known techniques to represent variability. Although, FM and OVM are successful
techniques for modeling variability, some challenges still need to be considered such as
logical inconsistency, dead features, propagation and delete-cascade, and explanation and
corrective recommendation. Inconsistency detection is defined as a challenging operation to
validate variability in [2]. In [27] the source of logical inconsistency is defined from a skill-
based or rule-based errors which would include errors made in touch-typing, in copying
values from one list to another, or other activities that frequently do not require a high level
of cognitive effort. One of the main drawbacks coming from the fusion of several different
and partial views is logical inconsistency [9]. Dead feature is defined in [6] as a frequent case
of error in feature model- based variability. Instead of dead feature, we called it dead choice.
Modeling variability methods must consider constraint dependency rules to assure the
correctness of the decision. Propagation and delete cascade operation is proposed to support
auto selection of choices in the decision making process. Propagation and delete cascade
operation is a very critical operation in the semi-auto environment.
This paper defines a rule-based approach for representing and validating knowledge in
DSS. In addition to representing variability to capture knowledge in DSS, intelligent rules
are defined to validate the proposed knowledge representation. The proposed method
validates two parts. The first part is validating a decision repository in which a logical
inconsistency and dead choices are detected. The second part is validating the decision
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