generating model with uncertainty by means of jtl

Dipartimento di Ingegneria e Scienze

Università degli Studi dell’Aquiladell’Informazione e Matematica

Generating model with uncertainty by means of JTL

Gianni Rosa

In Model-Driven Engineering (MDE) bidirectionality in

transformations has been always regarded as a key

mechanism, but has rarely produced anticipated benefits.

Why?

…probably the main reason is the ambivalence concerning non-

bijectivity.

Propagating changes from one side to the other is typically non univocal as more than one correct solution is admitted.

We discuss how dealing with multiple solutions is important and requires

specialized tools and support

Gianni Rosa – University of L’Aquila - ITALY

5Non-bijectivityMost examples of bidirectional transformations are non-bijective, therefore there may be multiple ways to transform two models into a consistent state, introducing uncertainty and non-determinism.

T-1

Δ Manual Changes

TMs Mt

Mt’

Ms1’

…?Msn’

Ms1’

Source model Target model

Hierarchical State Machine

Non-hierarchical state machine obtained by flattening the source model

T


Δ Manual ChangesThe designer performs

some manual changes on the generated model

T


Δ Manual ChangesThe designer performs

some manual changes on the generated model

TProblem

How to back propagate the manual changes on the target model towards the source

models according to the knowledge encoded in T?

What about non-bijectivity?


9Specifying transformation with JTLFragment of the HSM2NHSM transformation specified in JTL

9

transformation hsm2nhsm(source : HSM, target : NHSM) { top relation StateMachine2StateMachine { enforce domain source sSM : HSM::StateMachine; enforce domain target tSM : NHSM::StateMachine; }

top relation State2State { enforce domain source sourceState : HSM::State; enforce domain target targetState : NHSM::State; when { sourceState.owningCompositeState.oclIsUndefined(); } }

top relation CompositeState2State { enforce domain source sourceState : HSM::CompositeState; enforce domain target targetState : NHSM::State; }}


10




Specifying transformation with JTLFragment of the HSM2NHSM transformation specified in JTL

10

c

It transforms hierarchical state machines into flat state machines

and the other way round.


11





11

c

The forward transformation is clearly non-injective:

both «State» and «CompositeState» are mapped

to the same target «State»


12





12

c

The forward transformation is clearly non-injective:

both «State» and «CompositeState» are mapped

to the same target «State»


Δ Manual Changes

T

T

Modifications on the target are back propagated to the source which is consistently updated making use of tracing information


14Combinatorial explosionNot surprisingly, there is not a unique way of updating the source model.Despite the changes on the target model are relatively simple, their impact on the source model istypically exponential.


17UncertaintyUncertainty is a consequence of non-determinism.

Dealing with a multitude of models obtained by a non-deterministic transformation requires us to consider uncertainty as a first-class concern.

For any metamodel M an uncertainty metamodel U(M) can obtained by means of an automated transformation:

U: Ecore Ecore


18Uncertainty metamodel

.HSM

M

etam

odel

U(H

SM)

Met

amod

el



.HSM

M

etam

odel

U(H

SM)

Met

amod

el

the abstract metaclass TracedClass with attributes trace and ref is added



.HSM

M

etam

odel

U(H

SM)

Met

amod

el


StateMachine is generalized by TracedClass



.HSM

M

etam

odel

U(H

SM)

Met

amod

el


a direct sub-metaclass of StateMachine is added




.HSM

M

etam

odel

U(H

SM)

Met

amod

el


a direct sub-metaclass of StateMachine is added


The composition enable the representation of a point of uncertainty and its alternatives

the cardinality of attributes and references are relaxed


23Extension of JTL SemanticThe JTL semantics has been extended in order to factorize the solution space and generate a model with uncertainty instead of a set of models.

Example


27ConclusionThe JTL semantics has been refined in order to be able to generate directly the model with uncertainty semantically corresponding to the complete solution space.

We distinguish among two different behaviors: - extensional, generate all the models satisfying the relation defined in the bidirectional transformation; - intensional (or with uncertainty), generate a model with uncertainty wich is semantically equivalent to the models of the extensive case

The approach is implemented on Eclipse/EMF.

Thank you!