Comparing Approaches to Implement
Feature Model Composition
Mathieu Acher1, Philippe Collet1, Philippe Lahire1, Robert France2
1 University
of Nice Sophia Antipolis (France),
Modalis Team (CNRS, I3S Laboratory)
2 Computer Science Department,
Colorado State University

Context: Managing Variability
• Constructing a Repository of Medical Imaging Algorithms
– deployable on Grid infrastructures
– services embed the business code and are invoked remotely
through standardized protocol
• Highly Parameterized Services
– efficiently extend, change, customize, or configure services for
use in a particular context
– reusability and composability
– service as software product line (SPL)
• (SOAPL’08, MICCAI-Grid’08)
Comparing Approaches to Implement
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Feature Model Composition

Context: Managing Variability
• Constructing a Repository of Medical Imaging Services
– Deployable on Grid infrastructures
– Services embed the business code (e.g., algorithms) and are invoked
remotely through standardized protocol
• Highly Parameterized Services
Medical Image
Modality Acquisition Format Anonymized
MRI CT SPEC PET DICOM Nifti Analyze
And-Group Xor-Group
T1 T2 Optional Or-Group
Mandatory
Comparing Approaches to Implement
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Feature Model Composition

Context: Managing Variability
• Constructing a Repository of Medical Imaging Services
– Deployable on Grid infrastructures
– Services embed the business code (e.g., algorithms) and are invoked
remotely through standardized protocol
• Highly Parameterized Services
Medical Image
Registration
Modality Acquisition Format Anonymized
MRI CT SPEC PET DICOM Nifti Analyze
And-Group Xor-Group
T1 T2 Optional Or-Group
Transformation Method Interactive
Mandatory
Linear Non Grid Spatial Frequency
And-Group Xor-Group
Optional Or-Group
Rotation Scaling Affine Mandatory
Comparing Approaches to Implement
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Feature Model Composition

Context: Managing Variability
• Constructing a Repository of Medical Imaging Services
– Deployable on Grid infrastructures
– Services embed the business code (e.g., algorithms) and are invoked
remotely through standardized protocol
• Highly Parameterized Services
Medical Image
GridComputingNode
Modality Acquisition Format Anonymized
MRI CT SPEC PET DICOM Nifti Analyze
And-Group Xor-Group
T1 T2 Optional
Mandatory
Or-Group Operating System Processor FileSizeLimit
Registration
Windows Linux x32 x64
Transformation Method Interactive
And-Group Xor-Group
Linear Non Grid Spatial Frequency
Optional Or-Group
And-Group Xor-Group
Optional Or-Group Mandatory
Rotation Scaling Affine Mandatory
Comparing Approaches to Implement
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Feature Model Composition

Context: Managing Variability
• Constructing a Repository of Medical Imaging Services
– Deployable on Grid infrastructures
– Services embed the business code (e.g., algorithms) and are invoked
remotely through standardized protocol
• Highly Parameterized Services
Medical Image GridComputingNode
Modality Acquisition Format Anonymized
Operating System Processor FileSizeLimit
MRI CT SPEC PET DICOM Nifti Analyze
Windows Linux x32 x64
And-Group Xor-Group
T1 T2 Optional Or-Group And-Group Xor-Group
Mandatory
Optional Or-Group QoS
Mandatory
Registration
NetworkProtocol
Measurement Dimension Dynamic
Transformation Method Interactive HeaderEncoding Format Cryptographic Reliability Time
And-Group Xor-Group
Linear Non Grid Spatial Frequency XML HTTP Optional Or-Group
Mandatory
And-Group Xor-Group And-Group Xor-Group
Optional Optional Or-Group
Or-Group
Mandatory Mandatory
Rotation Scaling Affine
Comparing Approaches to Implement
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Feature Model Composition

Issues in Variability Modeling
• Current variability modeling techniques often do not scale up
to SPLs with a large number of features.
Scalability issues in terms of
- construction
- evolution
- reasoning
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Feature Model Composition

Separation of Concerns in SPLs
• Large and monolithic variability model
– Use smaller models representing the variability of
well-identified concerns.
– When variability models are separated…
composition operators are needed.
• In earlier work, we proposed a set of composition
operators for feature models (SLE’09)
• In this work, we focus on the merge operator
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Feature Model Composition

Purpose and Intended Audience
• An efficient, accurate implementation to
automatically merge feature models
• Our interest here:
– determine how (MBE/AOM/specific) techniques
perform with feature model merging implementation
– and which techniques are the most suitable.
• Intended audience:
– (1) SPL researchers working on feature modeling
techniques or developers of feature modeling tools ;
– (2) researchers/practitioners involved in the
MBE/AOM community
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Feature Model Composition

Agenda
• Background and Motivation
– Feature models and Merge operators
• Requirements for Merge Operators
– Criteria
• Comparison of Different Approaches
– Results
• Conclusion
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Feature Model Composition

Background: Feature Models
• Hierarchy + Variability
– Mandatory features, Optional features
– Alternatives and Constraints
Medical Image
Modality Acquisition Format Anonymized
MRI CT SPEC PET DICOM Nifti Analyze
And-Group Xor-Group
T1 T2 Optional Or-Group
Comparing Approaches to Implement
Mandatory
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Feature Model Composition

Background: Feature Models
• Hierarchy + Variability
– Mandatory features, Optional features
– Alternatives and Constraints
Medical Image
Modality Acquisition Format Anonymized
MRI CT SPEC PET DICOM Nifti Analyze
And-Group Xor-Group
T1 T2 Optional Or-Group
Mandatory
Comparing Approaches to Implement
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Feature Model Composition

Merge Operator: Principles
MRI MRI
T1 T2
T1 T2
When two feature models (FMs) share several features,
there is a need to merge the overlapping parts.
Comparing Approaches to Implement
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Feature Model Composition

Merge Operator: Principles
MRI MRI
T1 T2
FM1 FM2
T1 T2
Semantics ? MRI
properties to
preserve T1 T2
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Feature Model Composition

Merge Operator: Union
MRI MRI
FM1 FM2
T1 T2
T1 T2
{
{MRI, T1}, {
{MRI, T2} {MRI, T1},
} {MRI, T1, T2}
}
MRI
{{MRI, T1},
{MRI, T1, T2}, T1 T2
{MRI, T2}}
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Feature Model Composition

Merge Operator: Intersection
MRI MRI
FM1 FM2
T1 T2
T1 T2
{ {
{MRI, T1}, {MRI, T1},
{MRI, T2} {MRI, T1, T2}
} MRI }
{
{MRI, T1}
} T1
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Feature Model Composition

Merge Operator: Requirements (1)
OK
no!
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Feature Model Composition

Merge Operator: Requirements (1)
Not optimal
OK
OK Nifti is a “dead”
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Feature Model Composition feature 18

Merge Operator: Requirements (1)
Everything is
OK Comparing Approaches to Implement
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Feature Model Composition

Merge Operator: Requirements (2)
“Managing Variability in Workflow with Feature Model Composition Operators“
Software Composition (SC) conference 2010
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Feature Model Composition

Merge Operator: Requirements (3)
The ability of the merge operator to deal with several kinds of input FMs
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Feature Model Composition

Merge Operator: Requirements (4)
Aspects of the Implementation
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Feature Model Composition

Now the competition can start!
• Separate FMs
• AGG
• Kompose
• Kermeta
• Boolean Logic
– Large spectrum: From modeling/composition
techniques to FM specific solutions
– Some approaches have been proposed by other
researchers
Comparing Approaches to Implement
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Feature Model Composition

Separate FMs and Intersection
Base Aspect
Schobbens’ et al. 2007
1. Prime features
2. pp’
3. Root R with And-group
{{R, A, A’, B, B’}}
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Feature Model Composition

Separate FMs and Intersection
Base Aspect
{{R, A, A’, B, B’}} --
+ --
--
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Feature Model Composition

Separate FMs and Intersection
Base Aspect
+
- Comparing Approaches to Implement
Feature Model Composition
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Separate FMs and Intersection
Base Aspect
++
++
++ Comparing Approaches to Implement
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Feature Model Composition

Separate FMs and Intersection
Base Aspect
++
++
++ Comparing Approaches to Implement
Feature Model Composition
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AGG
• Attributed Grammar Graph
• Graph Transformation
– Left- Hand Side (LHS): source graph
– Right-Hand Side (RHS): target graph
• Catalogue of merge rules (Segura et al. 2007)
– Only for Union mode
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Feature Model Composition

AGG and a non-trivial example
(Intersection mode)
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Feature Model Composition

On the Difficulties of AGG
• The semantics properties currently implemented are limited
to the merge in union mode
– The intersection mode remains particularly challenging to be
implemented.
• Strategy based on patterns is difficult to realize
– AGG expressiveness: non recursive patterns
• Negative application conditions can precisely locate the
source of errors.
+
--
-
-
-
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Feature Model Composition

Kompose
• Generic composition tool (Fleurey, R. France et al.)
• Two major phases:
– (1) Matching phase identifies model elements that describe the same
concepts in the input models to be composed;
– (2) In the Merging phase, matched model elements are merged to
create new elements in the resulting model.
• Each element type has a signature Feature and Operator
– two elements with equivalent signatures are merged.
(Union mode)
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Feature Model Composition

Kompose and a non trivial example
• Two major phases:
– (1) Matching phase identifies model elements that describe the same
concepts in the input models to be composed;
– (2) In the Merging phase, matched model elements are merged to
create new elements in the resulting model.
(Intersection mode)
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Feature Model Composition

On the Difficulties of Kompose
• Compositional approach structured in two-stages (matching and merging)
is too restrictive for implementing an FM-specific merge operator.
• Recursive detection of matching elements is not sufficient since we need a
more global vision to decide whether elements should be merged or not
– Post-conditions: Hard to implement
– As Kompose implies local reasonning, handling constraints is not
conceivable as well
(Intersection mode)
- -
--
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Feature Model Composition

Experience with Kermeta
• Executable, imperative and object-oriented (meta-)modeling
language
– Kompose is built on top of Kermeta
– we apply the same strategy as with Kompose but without
strictly following the compositional approach
• We gain some benefits, notably a better cover of semantics
properties. Now that global and more complex reasoning is
possible, some features are not necessary added and less
FM errors are generated.
• There is still an issue when dealing with different
hierarchies.
• Finally, the handling of constraints appears to be
unpractical.
-
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Feature Model Composition

Boolean Logic
• The set of configurations represented by a FM can be
described by a propositional formula defined over a
set of Boolean variables
{{A, B}, {A, B, C}}
– A & (A<=>B) & (C=>A)
• We can define the Intersection mode
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Feature Model Composition

Boolean Logic
• We have only a Boolean formula: where is the
hierarchy? the variability information?
• Czarnecki et al. precisely propose an algorithm to
construct a FM from Boolean formula (SPLC’07)
– The algorithm constructs a tree with additional nodes for
feature groups that can be translated into a basic FM.
– We preliminary simplify the formula
• If φ ∧ f is unsatisfiable, the feature F is dead and can be
removed.
• The feature F can be identified as a full mandatory feature if
φ ∧ ¬f is unsatisfiable.
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Feature Model Composition

Boolean Logic: Strengths and Current
Limits
• Experiment on a set of input FMs sharing a same set of features and a
same hierarchy.
– The algorithm indicates all parent-child relationships (mandatory features) and
all possible optional subfeatures such that the hierarchy of the merged FM
corresponds to hierarchies of input FMs.
– And-group, Or-group and X or-group can be efficiently restored in the resulting
FM when it was necessary.
• Strengths
– The semantics properties are by construction respected.
– The technique does not introduce FM errors or does not increase unnecessary
the number of features.
– Constraints in FMs can be expressed using the full expressiveness of Boolean
logic and different sets of features can be manipulated.
– Apriori detection of error: formula is unsatisfiable
• Current Limits
– Hierarchy Mismatch
– Explanation
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Feature Model Composition

Results
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Feature Model Composition

Model-based composition techniques
• Difficulties. Why?
• The merge of FM is not purely structural
– You cannot focus on syntactical properties
– Semantical transformation or Semantics Preserving
Model Composition are needed
• A new challenge for modeling tools?
– Of course, modeling solutions can be revisited
• Other modeling approaches and technologies can be
considered and may emerge to outperform the solutions
considered in this paper.
• e.g., Using another Graph Transformation language
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Feature Model Composition

Conclusion
• The implementation of a merge operator for FMs
is an interesting challenge:
– We defined a set of criteria to systematically evaluate
an implementation
– We compared MBE/AOM/state-of-the-art techniques
– We proposed a solution based on Boolean logic that
fulfills most of the criteria
• and raises some limitations of our earlier work
• Future Work
– Open Issues
– diff and refactoring operations for FMs.
– Practical use of merge operators in different domains
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Feature Model Composition

?

Related Work
• Schobbens, P.Y., Heymans, P., Trigaux, J.C., Bontemps, Y.: Generic semantics of
feature diagrams. Comput. Netw. 51(2) (2007) 456–479
• Segura, S., Benavides, D., Ruiz-Cortés, A., Trinidad, P.: Automated merging of
feature models using graph transformations. Post-proceedings of the Second Sum-
mer School on GTTSE 5235 (2008) 489–505
• Fleurey, F., Baudry, B., France, R.B., Ghosh, S.: A generic approach for automatic
model composition. In Giese, H., ed.: MoDELS Workshops, Springer (2007) 7–15
• Reddy, Y.R., Ghosh, S., France, R.B., Straw, G., Bieman, J.M., McEachen, N., Song, E.,
Georg, G.: Directives for composing aspect-oriented design class models.
Transactions on Aspect-Oriented Software Development 3880 (2006) 75–105
• Czarnecki, K., Wasowski, A.: Feature diagrams and logics: There and back again. In:
SPLC 2007. (2007) 23–34
• Acher, M., Collet, P., Lahire, P., France, R.: Composing Feature Models. In: 2nd Int’l
Conference on Software Language Engineering (SLE’09). LNCS (2009) 20
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Feature Model Composition

A non-trivial example
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Feature Model Composition