comparing approaches to implement feature model composition

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)


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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



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AnonymizedFormat

DICOM Nifti Analyze

Modality Acquisition

MRI CT SPEC

T1 T2

PET

Medical Image

And-Group

Optional

Mandatory

Xor-Group

Or-Group






4

AnonymizedFormat

DICOM Nifti Analyze


MRI CT SPEC

T1 T2

PET

Medical Image

And-Group

Optional

Mandatory

Xor-Group

Or-Group InteractiveMethod

Spatial Frequency

Transformation

Linear

Rotation Affine

Non Grid

Registration

And-Group

Optional

Mandatory

Xor-Group

Or-Group

Scaling






5

AnonymizedFormat

DICOM Nifti Analyze


MRI CT SPEC

T1 T2

PET

Medical Image

And-Group

Optional

Mandatory

Xor-Group

Or-Group

InteractiveMethod

Spatial Frequency

Transformation

Linear

Rotation Affine

Non Grid

Registration

And-Group

Optional

Mandatory

Xor-Group

Or-Group

Scaling

FileSizeLimitProcessor

x32 x64

Operating System

Windows Linux

GridComputingNode

And-Group

Optional

Mandatory

Xor-Group

Or-Group






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AnonymizedFormat

DICOM Nifti Analyze


MRI CT SPEC

T1 T2

PET

Medical Image

And-Group

Optional

Mandatory

Xor-Group

Or-Group

InteractiveMethod

Spatial Frequency

Transformation

Linear

Rotation Affine

Non Grid

Registration

And-Group

Optional

Mandatory

Xor-Group

Or-Group

Scaling

FileSizeLimitProcessor

x32 x64

Operating System

Windows Linux

GridComputingNode

And-Group

Optional

Mandatory

Xor-Group

Or-Group

CryptographicFormat

XML HTTP

HeaderEncoding

NetworkProtocol

And-Group

Optional

Mandatory

Xor-Group

Or-Group

DynamicDimension

Reliability Time

Measurement

QoS

And-Group

Optional

Mandatory

Xor-Group

Or-Group

Issues in Variability Modeling

• Current variability modeling techniques often do not scale up to SPLs with a large number of features.


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Scalability issues in terms of- construction- evolution- reasoning

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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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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Agenda

• Background and Motivation

– Feature models and Merge operators

• Requirements for Merge Operators

– Criteria

• Comparison of Different Approaches

– Results

• Conclusion


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Background: Feature Models

• Hierarchy + Variability

– Mandatory features, Optional features

– Alternatives and Constraints


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AnonymizedFormat

DICOM Nifti Analyze


MRI CT SPEC

T1 T2

PET

Medical Image

And-Group

Optional

Mandatory

Xor-Group

Or-Group

Background: Feature Models

• Hierarchy + Variability

– Mandatory features, Optional features

– Alternatives and Constraints


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AnonymizedFormat

DICOM Nifti Analyze


MRI CT SPEC

T1 T2

PET

Medical Image

And-Group

Optional

Mandatory

Xor-Group

Or-Group

Merge Operator: Principles


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When two feature models (FMs) share several features, there is a need to merge the overlapping parts.

T2

MRI

T1T2

MRI

T1

Merge Operator: Principles


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FM2

?Semantics properties to preserve

FM1

T2

MRI

T1T2

MRI

T1

T2

MRI

T1

Merge Operator: Union


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FM2T2

MRI

T1FM1

T2

MRI

T1

{{MRI, T1}, {MRI, T1, T2}}

{{MRI, T1}, {MRI, T2}}

{{MRI, T1},{MRI, T1, T2},{MRI, T2}}

T2

MRI

T1

Merge Operator: Intersection


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FM2T2

MRI

T1FM1

T2

MRI

T1

MRI

T1

{{MRI, T1}, {MRI, T1, T2}}

{{MRI, T1}, {MRI, T2}}

{{MRI, T1}}

Merge Operator: Requirements (1)


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no!

OK



18

OK

OK

Not optimal

Nifti is a “dead” feature



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Everything is OK



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“Managing Variability in Workflow with Feature Model Composition Operators“Software Composition (SC) conference 2010


The ability of the merge operator to deal with several kinds of input FMs


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Aspects of the Implementation


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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


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Separate FMs and Intersection


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Base Aspect

1. Prime features2. pp’3. Root R with And-group

{{R, A, A’, B, B’}}

Schobbens’ et al. 2007



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Base Aspect

{{R, A, A’, B, B’}}

+

--

--

--



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Base Aspect

+

-



28

Base Aspect

++

++

++



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Base Aspect

++

++

++

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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AGG and a non-trivial example


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(Intersection mode)

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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---

--+

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 – two elements with equivalent signatures are merged.


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Feature and Operator

(Union mode)

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.


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(Intersection mode)

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


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--- -

(Intersection mode)

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 differenthierarchies.

• Finally, the handling of constraints appears to be unpractical.


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-

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 & (A<=>B) & (C=>A)

• We can define the Intersection mode


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{{A, B}, {A, B, C}}

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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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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Results


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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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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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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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A non-trivial example


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comparing approaches to implement feature model composition

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