SEKE 2012 Proceedings - Knowledge Systems Institute

Third, we present the feature model of the graph editor by adding
assertions (refer to the tutorial 5 for use ofprotégé).
Last, we reason about feature model to verify its consistency and
completeness by using reasoner and SPARQL.
We get feature instances whose state is conflict by following
SPARQL: "SELECT ?x WHERE {?x hasState conflict}"
Case 1: Suppose ABox A={Feature(graphManipulate),
Feature(graphDelete), Feature(graphSelect), hasState(graphDelete,
bound), hasState(graphSelect,removed), require(graphDelete,
graphSelect)}. Using SPARQL, we find two conflict feature instances
graphDelete and graphSelect according to require-rule2, require-rule3
and conflict-rule.
Case 2: Suppose ABox A={ Feature(graphMove), DimFeature
(movingMode) , hasState(movingMode,bound), hasMandatoryPart
(graphMove, movingMode), DimFeature(graphDim), DimValue(2D),
DimValue(3D), hasState(3D,bound), hasAlternativeValue
(graphDim,2D), hasAlternativeValue(graphDim,3D), mutex
(graphMove, 3D)}. Using SPARQL, we find three conflict feature
instances 3D, graphMove and movingMode according to mutex-rule,
require-rule3 and conflict-rule. Feature instances 3D, graphMove are
conflict because they are mutually exclusive. The feature instance
movingMode is conflict, because new assertion
“hasState(movingMode,removed)” is derived according to requirerule3,
the assertion is inconsistent with “hasState(movingMode,bound)”.
By using reasoner, we can verify the consistency of feature model,
or achieve new assertions to make the feature model more complete.
V. RELATED WORK
Current methods have some limitations. The relationship between
features and constraints among them are similar but different. For
example, the variation-variant features (in FeatuRSEB [3]) are
equivalent to dimension-value features (in FODM [10]); and XORrelation
(in FeatuRSEB) and alternative-relation (in Czarnecki’s
proposal [13]) are similar. Furthermore, most proposals depict feature
models in a grapy way by using slightly different notations. They are
essential and easy to comprehension. However, they lack semantic, and
this has led to the redundancy and confusion.
Ontology-Oriented requirement analysis (OORA) [14] method
enhances the object-oriented analysis and design. In FODM,
propositional logic is selected separately to model and verify constraint
relations. Kaiya et al [15] proposed a method of software requirements
analysis based on ontologies, where inference rules is established to
detect incompleteness and inconsistency in a specification. Peng et al [8]
proposed an ontology-base feature meta-model supporting applicationoriented
tailoring. Some constraints rules are introduced to validate
constraints by ontology reasoning. Benavides et al [9] used constraint
programming to model and reason on a SPL. The model need to be
extended to support dependencies such as requires or excludes relation.
To the best of our knowledge [16], there are only small numbers of
proposals that treat automatic reasoning about feature models to verify
the constraints. Many ontology-based approaches do not differentiate
between feature meta-model and feature models.
VI. CONCLUSIONS
In this paper, we propose a DLs-based method to model feature:
describing feature meta-model with concepts, roles, axioms and rules in
TBox, while describing feature model with assertions in ABox. We can
reason about the feature model to verify the consistency and
completeness. A case study in graph editor domain shows that this
method will be beneficial.
The main features of this method are as follows: i) our feature
model is compatible with the model that adopted by most of domain
engineering methods. ii) The explicit semantic clarifies the similarity
and differences among these methods. iii) Concrete feature models are
instantiated in ABox, so it is convenient to perform running-time
verification. Still there are some weaknesses: some non-functional
features are not taken into considerations; how to elicit feature in a
domain depends on expertise experience.
ACKNOWLEDGMENT
This research was supported by the National High-Tech R&D
Program of China (No. 2009AA010307), and by Fundamental
Research Funds for the Central Universities (No. NS2012022). We
thank Prof. David Parnas, Jie Chen for their helpful suggestions.
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