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Model Transformation Reuse

The document summarizes model-driven engineering (MDE) and discusses approaches to reuse in MDE transformations. Specifically: - MDE aims to increase abstraction in software development by modeling at a higher level of abstraction rather than coding directly. Models are used to describe problems, simulate/verify/test, and generate code. - Reusing MDE artifacts like transformations is challenging as they are defined for specific meta-models. Current practice involves ad-hoc copying and adapting transformations, which is error-prone. - The document presents three approaches to improve reuse: concepts, multi-level modeling, and a-posteriori typing. Concepts define transformations at a more abstract level and allow automated adaptation.

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TRANSFORMATION REUSE IN MODEL-DRIVEN ENGINEERING McMaster CS Seminar. Jan 22 2018. Juan de Lara joint work with Esther Guerra and Jesús Sánchez Cuadrado Modelling&Software Engineering Research Group http://miso.es @miso_uam
MODEL DRIVEN ENGINEERING (MDE) Increase the level of abstraction in software development Models are actively used to • Describe the problem… • Simulate/verify/test… • Generate code for the final application Move from the solution space (code-centric) to the problem space • Higher levels of productivity and quality • Less accidental details 2
MODEL DRIVEN ENGINEERING (MDE) For specific domains • Avoid coding the same solutions over and over • Families of applications Domain-Specific Modelling Languages (DSLs) Code Generator • Modelling, validation and automatic generation of telephony services 3
BENEFITS OF MDE Increase productivity • Domain-Specific instead of General Purpose languages • Reported productivity increases in the order of 20-30% [WH14] Automation • Avoid repetitive tasks Higher level of abstraction • Domain models vs low-level coding • More intentional, easier to analyse • Less accidental details End-user development • By Domain-Specific Languages 4 Whittle, Hutchinson, Rouncefield. “The state of practice in model-driven engineering”. IEEE Software 31(3): 79-85 (2014)
DRAWBACKS OF MDE High initial investment • Meta-models • Modelling environments • Transformations • Code generators • Simulators • Model-to-Model transformations 5 Model Code Modeli Modeli+1 Model Model’ Meta- model Meta- model Meta- models Meta- modelt«iOf» «iOf» «iOf» «iOf» «iOf» Code generation In-place transformation Model-to-model trasformation
OUR GOAL 6 Make MDE more widely applicable by reducing its required initial investment MDE artefacts (transformations, code generators, editors) are defined over specific meta-models • Difficult to reuse for other related meta-models How to avoid creating (essentially) the same transformation for different meta-models?
IN THE REST OF THIS TALK… 7 Current practice of “reuse” • Ad-hoc reuse (clone and own) • Explicit model adaptation Three different approaches to reuse • Concepts • Multi-level modelling • A-posteriori typing Tool support Conclusions and future work
EXAMPLE: PROCESS MODELS TO PETRI NETS 8 Task name: String initial: boolean next * Place Transitionid: String tokens: int inps outs * *
9 press wait cross start start press wait cross Task name: String initial: boolean next * Place Transitionid: String tokens: int inps outs * * EXAMPLE: PROCESS MODELS TO PETRI NETS task2PN
10 L R=NAC t:Task name=n t:Task name=n :Place id=n tokens=t.initial?1:0 t1:Task t2:Task :next p1:Place p2:Place t1:Task t2:Task :next p1:Place p2:Place :Transition :inps :outs L R=NAC TRANSFORMATION (GRAPH BASED)
11 TRANSFORMATION (ATL) rule Task2Place { from t : Process!Task to p : PN!Place ( id <- t.name, tokens <- if t.initial then 1 else 0 endif ) } rule next2Transition { from t1 : Process!Task, t2 : Process!Task (t1.next->includes(t2)) to tr : PN!Transition ( inps <- t1, outs <- t2 ) } …
REUSE FOR ANOTHER META-MODEL 12 Task name: String initial: boolean next * Place Transitionid: String tokens: int inps outs * * Activity ident: String InitialActivity task2PN FlowEdge from to
13 rule Task2Place { from t : Process!Task to p : PN!Place ( id <- t.name, tokens <- if t.initial then 1 else 0 endif ) } rule next2Transition { from t1 : Process!Task, t2 : Process!Task (t1.next->includes(t2)) to tr : PN!Transition ( inps <- t1, outs <- t2 ) } … AD-HOC REUSE (CLONE&OWN)
AD-HOC REUSE (CLONE&OWN) 14 rule Task2Place { from t : Process!Activity to p : PN!Place ( id <- t.ident, tokens <- if t.oclIsTypeOf(Process!InitialActivity) then 1 else 0 endif ) } rule next2Transition { from t1 : Process!Activity, t2 : Process!Activity (Process!FlowEdge.allInstances()->exist(e | e.from=t1 and e.to=t2)) to tr : PN!Transition ( inps <- t1, outs <- t2 ) }
AD-HOC REUSE (CLONE&OWN) 15 rule Task2Place { from t : Process!Activity to p : PN!Place ( name <- t.ident, tokens <- if t.oclIsTypeOf(Process!InitialActivity) then 1 else 0 endif ) } rule next2Transition { from t1 : Process!Activity, t2 : Process!Activity (Process!FlowEdge.allInstances()->exist(e | e.from=t1 and e.to=t2)) to tr : PN!Transition ( inps <- t1, outs <- t2 ) } • Complex and error prone • Repetitive • Does not scale to large transformations
EXPLICIT MODEL ADAPTATION 1616 Task name: String initial: boolean next * Place Transitionid: String tokens: int inps outs * * Activity ident: String InitialActivity task2PN FlowEdge from to activity2task;task2PN activity2task
MOTIVATION 17 rule Activity2Task { from t : Activities!Activity to p : Process!Task ( id <- t.ident, initial <- t.oclIsKindOf(Activities!InitialActivity), next <- Activities!FlowEdge.allInstances()-> select (e | e.from = t)-> collect(e | e.to ) ) } t: InitialActivity ident=“a0” p: Task name=“a0” initial=true pl: Place name=“a0” tokens=1 activity2task task2PN
MOTIVATION 18 rule Activity2Task { from t : Activities!Activity to p : Process!Task ( name <- t.ident, initial <- t.oclIsKindOf(Activities!InitialActivity), next <- Activities!FlowEdge.allInstances()-> select (e | e.from = t)-> collect(e | e.to ) ) } • Three models involved, complicates traceability • Two transformations: less efficient • May require a transformation back to the initial model
19 Concept- based reuse
Automate ad-hoc copy-adaptation: CONCEPT-BASED REUSE 20 Concept Msrc Mtar from to «conforms to» «conforms to» Template execution MMsrc Instantiated template to binding MMtar HOT Transformation template definition from Final user Transformation developer (with reuse) Transformation developer (for reuse) 1 2 3 4 • Transformations defined over “concepts” • Binding to concrete meta-model • Automated transformation adaptation Sánchez Cuadrado, Guerra, de Lara. “A Component Model for Model Transformations”. IEEE Trans. Software Eng. 2014.
21 STRUCTURAL CONCEPTS Structural Concepts. • They gather the structure needed from meta-models for the transformation to be applicable. • They have the form of a meta- model as well. SimpleTask concept Task name: String initial: boolean next * Their elements are treated as variables, which need to be bound to elements in specific meta-models. Interface of the transformation • No superflous elements • No extra complexity • Transformations become as simple as possible
22 REUSABLE TRANSFORMATIONS Transformations defined using the types of the concept. L R=NAC t:Task name=n t:Task name=n :Place id=n tokens=t.initial?1:0 t1:Task t2:Task :next p1:Place p2:Place t1:Task t2:Task :next p1:Place p2:Place :Transition :inps :outs L R=NAC
23 BINDING Concept binding: • Bind each element in the concept to an element in the meta-model. Task SETask name  id initial  isInitial next  following Binding SETaskEngineer Coding Testing actors * Software Engineering processes Design SimpleTask concept Task name: String initial: boolean next * Analysis id: String isInitial: boolean following 0..5
ADAPTATION The transformation gets automatically adapted Similar to template instantiation in generic programming 24 t:SETask id=n t:SETask id=n :Place id=n tokens=t.isInitial?1:0 t1:SETask t2:SETask :following p1:Place p2:Place t1:SETask t2:SETask :following p1:Place p2:Place :Transition :inps :outs
IS THE RESULTING TRANSFORMATION CORRECT? Yes, by following some rules: Rules for the binding • Cardinalities • Composition, features, subtyping, etc Depends on what you do with the concept • Read only (e.g., for M2M transformation) • Write (e.g., for in-place transformation) 25 SETaskTask SETaskTask Concept Meta-modelbinding R W Sánchez Cuadrado, Guerra, de Lara. “Flexible Model-to-Model Transformation Templates: An Application to ATL”. JOT 2012. Task next * SETask R following 0..5 SETask following 0..5 Task next * W
26 MORE FLEXIBLE BINDINGS Concepts express a particular design choice on how structure is organized Different meta-models may implement the same structure differently • Association as intermediate class • Enumerate as subclases • etc. binding Task name: String initial: boolean next * Activity ident: String InitialActivity FlowEdge from to
27 BINDING ADAPTERS Elements in the concept are variables • Allow binding them to expressions: adapters Adapters resolve the heterogeneities between the concept and the meta-model They induce an adaptation of the transformation Sánchez Cuadrado, Guerra, de Lara. “A Component Model for Model Transformations”. IEEE Trans. Software Eng. 2014. de Lara, Guerra. “Towards the flexible reuse of model transformations: A formal approach based on graph transformation”. JLAMP 83(5-6). 427-458 (2014)
rule Task2Place { from t : Pr!Task to p : PN!Place ( tokens <- if t.initial then 1 else 0 endif )} 28 Task initial: boolean next * Concept Activity InitialActivity Flow Edge from to Task  Activity Task.initial  if (self.oclIsKindOf(InitialActivity)) then true else false endif Task.next  FlowEdge.allInstances()-> select(e|e.from=self)-> collect(to) Meta-model typed on helper context Pr!Activity def: initial : Boolean = if (…) then true else false endif; rule Task2Place { from t : Pr!Activity to p : PN!Place ( tokens <- if t.initial then 1 else 0 endif )} typed on adaptation
ON THE ROLE OF CONCEPTS… Aren’t concepts characterizing a set of “suitable” meta-models for the transformation? Isn’t that exactly what meta-models do (for models)? Concepts as meta-meta-models Rely on typing: no need to adapt the transformation 29
30 Reuse through multi- level modelling
Language families as instances of a common meta-model MULTI-LEVEL BASED REUSE 31 Process Modelling Software Process Modelling Language Educational Modelling Language Software Process Models Educational Models de Lara, Guerra, Sánchez Cuadrado. “Model-driven engineering with domain specific meta-modelling languages”. SoSyM 2015 (Springer).
32 MULTI-LEVEL BASED GENERICITY Transformations can be defined at the top meta-level Applicable to instances of all languages in the family Process Modelling Software Process Modelling Language Educational Modelling Language Software Process Models Educational Models … Transformation defined on applicableto
33 MULTI-LEVEL BASED GENERICITY Task Kind Gateway Kind Seq Par src tar * * Test: TaskKindCoding:TaskKind T2C: Seq C2T: Seq src tar srctar data: Coding unitDt: Test gui: Coding : C2T src tar : T2C tar src «conf» «conf» data: Coding unitDT: Test gui: Coding rule Task2Place transform t : Level0!TaskKind to p : Place { ... } .... defined on
rule Task2Place transform t : Level0!TaskKind to p : Place { ... } .... 34 MULTI-LEVEL BASED GENERICITY data: Coding unitDT: Test gui: Coding rule Coding2Place transform t : Coding to p : Place {…} rule Test2Place transform t : Test to p : Place {…} defined on data: Coding unitDt: Test gui: Coding : C2T src tar : T2C tar src «conf» Task Kind Gateway Kind Seq Par src tar * * Test: TaskKindCoding:TaskKind T2C: Seq C2T: Seq src tar srctar «conf»
35 MULTI-LEVEL VS CONCEPT- BASED GENERICITY Concept-based Genericity Multi-level-based Genericity • A posteriori binding: meta- models may exist first. • Adapters and hybrid concepts to solve heterogeneities. • A priori: meta-meta-model should exist first. • Ability to apply an operation several meta-levels below. • Domain-Specific meta- modelling.
36 CAN WE GET THE BEST OF BOTH APPROACHES? • A posteriori binding: meta-models may exist first. • Adapters or Hybrid concepts to solve heterogeneities. • Independence of the transformation language Concept-based reuse Multi-level reuse
37 CAN WE GET THE BEST OF BOTH APPROACHES? • A posteriori binding: meta-models may exist first. • Adapters to solve heterogeneities. • Independence of the transformation language Concept-based reuse Multi-level reuse Let’s make the typing relation: • A-posteriori • As flexible as adapters
38 Reuse through a- posteriori typing
A-POSTERIORI TYPING 39 A more flexible typing mechanism for MDE Decouple instantiation from classification • Interfaces in object-oriented programming • Roles in role-based programming languages Allow dynamic typing and multiple classifiers for objects Type and instance-level reclassification specifications de Lara, Guerra. A Posteriori Typing for Model-Driven Engineering: Concepts, Analysis, and Applications. ACM Trans. Softw. Eng. Methodol. 25 (4) 31:1-31:60 (2017)
CONSTRUCTIVE VS A-POSTERIORI TYPING 40 Task start: Date duration: int name: String review: Task start= 8/5/15 duration= 30 name= “rev” creation «instance of» Measurable quantity: int Schedulable date: Date review: Task Tasks meta-model model Scheduling MM Measuring MM model «instance of» «instance of» start= 8/5/15 duration= 30 name= “rev” «Schedulable,Measurable» Constructive typing A-posteriori typing Constructive typing A-posteriori typing
SPECIFYING AP TYPINGS AT THE TYPE-LEVEL 41 Schedulable date: Date span: double review: Task Tasks meta-model ( MMC ) start= 8/5/15 /months= 1 duration= 30 name= “rev” Scheduling MM ( MMR ) Task start: Date duration: int name: String Typing Specification Task  Schedulable self.start  date /months: double=self.duration/30  span «Schedulable» «date» «span» creation «instance of» (constructive) «instance of» (a-posteriori) • Derived attributes (months) defined in the typing specification. • Typing rules: ensure syntactic correctness
TYPE-LEVEL AP TYPING 42 date: Date span: double writing: Task start= 15/3/15 duration= 90 name= “wrt” review: Task start= 8/5/15 duration= 30 name= “rev” Schedulable Scheduling MM (MMR) Instance model of Tasks ( MMC ) «instance of» (a-posteriori) “Scheduling glasses” Operations review: Schedulable date= 8/5/15 span= 1 {review, writing} {1, 3} Schedulable.allInstances() writing: Schedulable date= 15/3/15 span= 3 Schedulable.allInstances()-> collect(span)
SPECIFYING AP TYPINGS AT THE INSTANCE LEVEL 43 date: Date span: double writing: Task start= 15/3/15 duration= 90 name= “wrt” review: Task start= 8/5/15 /months= 1 duration= 30 name= “rev” «Schedulable» «date» «instance of» (a-posteriori) Schedulable Scheduling MM (MMR) Typing Specification Task.allInstances()->select(duration<80)  Schedulable self.start  date /months: double=self.duration/30  span Instance model of Tasks ( MMC ) «span» • Typing defined by queries. • Derived attributes defined by queries as well.
INSTANCE-LEVEL AP TYPING 44 Operations date: Date span: double writing: Task start= 15/3/15 duration= 90 name= “wrt” review: Task start= 8/5/15 duration= 30 name= “rev” Schedulable Scheduling MM (MMR) Instance model of Tasks ( MMC ) «instance of» (a-posteriori) review: Schedulable date= 8/5/15 span= 1 Schedulable.allInstances()-> collect(span) {review} {1} Schedulable.allInstances() “Scheduling glasses”
INSTANCE-LEVEL AP TYPING 45 Operations date: Date span: double writing: Task start= 15/3/15 duration= 60 name= “wrt” review: Task start= 8/5/15 duration= 30 name= “rev” Schedulable Scheduling MM (MMR) Instance model of Tasks ( MMC ) «instance of» (a-posteriori) review: Schedulable date= 8/5/15 span= 1 Schedulable.allInstances()-> collect(span) {review, writing} {1, 2} Schedulable.allInstances() “Scheduling glasses” writing: Schedulable date= 15/3/15 span= 2
Activity  Task ident  name /init : boolean = self.oclsKindOf(InitialActivity)  initial /follow: Task[*] = FlowEdge.allInstances()-> select(e|e.from=self)-> collect(e|e.to)  next EXAMPLE 46 Task name: String initial: boolean next * Activity ident: String InitialActivity FlowEdge from to
A-POSTERIORI TYPING AS A TRANSFORMATION 47 Task name: String initial: boolean next Place Transitionid: String tokens: int inps outs * * task2PN Task.allInstances()  Place name  id /toks : int = if (self.initial) then 1 else 0  tokens Task.allInstances()->select(t|t.next.isDefined())  Transition /prevs : Task[*] = Set{self}  inps /nexts : Task[*] = Set{self.next}  outs 0..1
A-POSTERIORI TYPING AS A TRANSFORMATION 48 Place Transitionid: String tokens: int inps outs * * t1:Task name=“t1” initial=false name=“t2” initial=true :next name=“t3” initial=false :next t2:Task t3:Task Task name: String initial: boolean next 0..1 task2PN
A-POSTERIORI TYPING AS A TRANSFORMATION 49 Place Transitionid: String tokens: int inps outs * * «Place» «Place, Transition» «Place, Transition» «id» «id» «id» Task name: String initial: boolean next 0..1 task2PN t1:Task name=“t1” initial=false name=“t2” initial=true :next name=“t3” initial=false :next t2:Task t3:Task
A-POSTERIORI TYPING AS A TRANSFORMATION 50 Place Transitionid: String tokens: int inps outs * * t2:Task t3:Task «Transition» «Transition» «outs» «outs» «inps» «inps» Task name: String initial: boolean next 0..1 task2PN «Place» «Place» «id» «id»name=“t2” initial=true name=“t3” initial=false t2:Task t3:Task «Place» «id» t1:Task name=“t1” initial=false
T l support 51
BENTŌ: CONCEPT-BASED REUSE FOR ATL Adaptation of ATL transformations according to the binding Support for refactoring meta-models into concepts 52 Sánchez Cuadrado, Guerra, de Lara. “A Component Model for Model Transformations”. IEEE TSE 2014 Sánchez Cuadrado, Guerra, de Lara. “Reusable Model Transformation Components with bentō”. ICMT’ 15
// Model Tasks someTasks { Task t0 { start = "30/04/2015"; duration = 30; name = "coding"; } } METADEPTH 53 Multi-level textual modelling Concepts • Over every Epsilon language • Hybrid concepts A-posteriori typing • Over every Epsilon language Analysis of type-level specs • Integration with the USE validator • Bidirectional reclassification • Reclassification totality and surjectivity // Meta-model Model Tasks { Node Task { start : Date; duration : int; name : String; } }
CONCEPT-BASED REUSE 54 concept SimpleTasks(&M, &T, &initial) { Model &M { Node &T { &initial : boolean; name : String; } } } bind SimpleTasks( SEProcess, SEProcess::SETask, SEProcess::SETask.isInitial ) <<some operation>> rule Task2Place transform task : Source!&T to place : Target!Place { place.name := task.name; if (task.&initial=true) place.tokens:=1 else place.tokens:=0 } ETL transformation
55 Model ProcessModel@2 { Node Task { name@1 : String[0..1]; initial : boolean = false; } } ProcessModel SoftwareProcess { Task Analysis { name = “reqs, analysis”; } } SoftwareProcess aSoftProcess{ Analysis a { initial = true; } } @model(potency=0) rule Task2Place transform task : Source!Task to place : Target!Place { place.name := task.name; if (task.initial=true) place.tokens:=1 else place.tokens:=0 } ETL transformation MULTI-LEVEL BASED REUSE
56 type Factory PetriNet { Conveyor::parts > Place::tokens, Generator::outps > Transition::outs, Terminator::inps > Transition::ins, Assembler::inps > Transition::ins, Assembler::outps > Transition::outs, } A-POSTERIORI TYPINGS
Transformations defined over Petri nets can be reused “as is” for Factory • Provide an AP typing from Factory to Petri nets • Factory models become typed as Petri nets 57 // EOL excerpt of the simulator operation Transition enabled() : Boolean { return self.ins.forAll(p| p.tokens.size()>0); } operation step() : Boolean { var enabled : Set(Transition) := Transition.all.select( t | t.enabled()); ... // fire one random Transition from enabled } A-POSTERIORI TYPINGS
CONCLUSIONS 58 Lower entry-level to MDE to make it more widely used Approaches to reuse model transformations in MDE • Concept-based • Ideas from generic programming • Automates ad-hoc reuse • Multi-level modelling • Families of languages • Independent of transformation language • A-posteriori typing • Provide additional types via retyping specifications • Improve model-adaptation based reuse
FUTURE WORK 59 Product-Lines based reuse • With Salay, Chechik (UoT) • Analysis Requirement models as the interface of transformations • More powerful than concepts Exploit a-posteriori typing in MDE • Multiple typings for MDE • Retypings as bx transformations Unified approach?
THANKS! Juan.deLara@uam.es 60 http://www.miso.es joint work with E. Guerra and J. Sánchez Cuadrado @miso_uam

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Model Transformation Reuse

  • 1.
    TRANSFORMATION REUSE IN MODEL-DRIVEN ENGINEERING McMasterCS Seminar. Jan 22 2018. Juan de Lara joint work with Esther Guerra and Jesús Sánchez Cuadrado Modelling&Software Engineering Research Group http://miso.es @miso_uam
  • 2.
    MODEL DRIVEN ENGINEERING (MDE) Increasethe level of abstraction in software development Models are actively used to • Describe the problem… • Simulate/verify/test… • Generate code for the final application Move from the solution space (code-centric) to the problem space • Higher levels of productivity and quality • Less accidental details 2
  • 3.
    MODEL DRIVEN ENGINEERING (MDE) Forspecific domains • Avoid coding the same solutions over and over • Families of applications Domain-Specific Modelling Languages (DSLs) Code Generator • Modelling, validation and automatic generation of telephony services 3
  • 4.
    BENEFITS OF MDE Increaseproductivity • Domain-Specific instead of General Purpose languages • Reported productivity increases in the order of 20-30% [WH14] Automation • Avoid repetitive tasks Higher level of abstraction • Domain models vs low-level coding • More intentional, easier to analyse • Less accidental details End-user development • By Domain-Specific Languages 4 Whittle, Hutchinson, Rouncefield. “The state of practice in model-driven engineering”. IEEE Software 31(3): 79-85 (2014)
  • 5.
    DRAWBACKS OF MDE Highinitial investment • Meta-models • Modelling environments • Transformations • Code generators • Simulators • Model-to-Model transformations 5 Model Code Modeli Modeli+1 Model Model’ Meta- model Meta- model Meta- models Meta- modelt«iOf» «iOf» «iOf» «iOf» «iOf» Code generation In-place transformation Model-to-model trasformation
  • 6.
    OUR GOAL 6 Make MDEmore widely applicable by reducing its required initial investment MDE artefacts (transformations, code generators, editors) are defined over specific meta-models • Difficult to reuse for other related meta-models How to avoid creating (essentially) the same transformation for different meta-models?
  • 7.
    IN THE RESTOF THIS TALK… 7 Current practice of “reuse” • Ad-hoc reuse (clone and own) • Explicit model adaptation Three different approaches to reuse • Concepts • Multi-level modelling • A-posteriori typing Tool support Conclusions and future work
  • 8.
    EXAMPLE: PROCESS MODELS TOPETRI NETS 8 Task name: String initial: boolean next * Place Transitionid: String tokens: int inps outs * *
  • 9.
    9 press wait cross start start press wait cross Task name: String initial: boolean next *Place Transitionid: String tokens: int inps outs * * EXAMPLE: PROCESS MODELS TO PETRI NETS task2PN
  • 10.
  • 11.
    11 TRANSFORMATION (ATL) rule Task2Place { fromt : Process!Task to p : PN!Place ( id <- t.name, tokens <- if t.initial then 1 else 0 endif ) } rule next2Transition { from t1 : Process!Task, t2 : Process!Task (t1.next->includes(t2)) to tr : PN!Transition ( inps <- t1, outs <- t2 ) } …
  • 12.
    REUSE FOR ANOTHER META-MODEL 12 Task name:String initial: boolean next * Place Transitionid: String tokens: int inps outs * * Activity ident: String InitialActivity task2PN FlowEdge from to
  • 13.
    13 rule Task2Place { fromt : Process!Task to p : PN!Place ( id <- t.name, tokens <- if t.initial then 1 else 0 endif ) } rule next2Transition { from t1 : Process!Task, t2 : Process!Task (t1.next->includes(t2)) to tr : PN!Transition ( inps <- t1, outs <- t2 ) } … AD-HOC REUSE (CLONE&OWN)
  • 14.
    AD-HOC REUSE (CLONE&OWN) 14 ruleTask2Place { from t : Process!Activity to p : PN!Place ( id <- t.ident, tokens <- if t.oclIsTypeOf(Process!InitialActivity) then 1 else 0 endif ) } rule next2Transition { from t1 : Process!Activity, t2 : Process!Activity (Process!FlowEdge.allInstances()->exist(e | e.from=t1 and e.to=t2)) to tr : PN!Transition ( inps <- t1, outs <- t2 ) }
  • 15.
    AD-HOC REUSE (CLONE&OWN) 15 ruleTask2Place { from t : Process!Activity to p : PN!Place ( name <- t.ident, tokens <- if t.oclIsTypeOf(Process!InitialActivity) then 1 else 0 endif ) } rule next2Transition { from t1 : Process!Activity, t2 : Process!Activity (Process!FlowEdge.allInstances()->exist(e | e.from=t1 and e.to=t2)) to tr : PN!Transition ( inps <- t1, outs <- t2 ) } • Complex and error prone • Repetitive • Does not scale to large transformations
  • 16.
    EXPLICIT MODEL ADAPTATION 1616 Task name:String initial: boolean next * Place Transitionid: String tokens: int inps outs * * Activity ident: String InitialActivity task2PN FlowEdge from to activity2task;task2PN activity2task
  • 17.
    MOTIVATION 17 rule Activity2Task { fromt : Activities!Activity to p : Process!Task ( id <- t.ident, initial <- t.oclIsKindOf(Activities!InitialActivity), next <- Activities!FlowEdge.allInstances()-> select (e | e.from = t)-> collect(e | e.to ) ) } t: InitialActivity ident=“a0” p: Task name=“a0” initial=true pl: Place name=“a0” tokens=1 activity2task task2PN
  • 18.
    MOTIVATION 18 rule Activity2Task { fromt : Activities!Activity to p : Process!Task ( name <- t.ident, initial <- t.oclIsKindOf(Activities!InitialActivity), next <- Activities!FlowEdge.allInstances()-> select (e | e.from = t)-> collect(e | e.to ) ) } • Three models involved, complicates traceability • Two transformations: less efficient • May require a transformation back to the initial model
  • 19.
  • 20.
    Automate ad-hoc copy-adaptation: CONCEPT-BASEDREUSE 20 Concept Msrc Mtar from to «conforms to» «conforms to» Template execution MMsrc Instantiated template to binding MMtar HOT Transformation template definition from Final user Transformation developer (with reuse) Transformation developer (for reuse) 1 2 3 4 • Transformations defined over “concepts” • Binding to concrete meta-model • Automated transformation adaptation Sánchez Cuadrado, Guerra, de Lara. “A Component Model for Model Transformations”. IEEE Trans. Software Eng. 2014.
  • 21.
    21 STRUCTURAL CONCEPTS Structural Concepts. • Theygather the structure needed from meta-models for the transformation to be applicable. • They have the form of a meta- model as well. SimpleTask concept Task name: String initial: boolean next * Their elements are treated as variables, which need to be bound to elements in specific meta-models. Interface of the transformation • No superflous elements • No extra complexity • Transformations become as simple as possible
  • 22.
    22 REUSABLE TRANSFORMATIONS Transformations defined usingthe types of the concept. L R=NAC t:Task name=n t:Task name=n :Place id=n tokens=t.initial?1:0 t1:Task t2:Task :next p1:Place p2:Place t1:Task t2:Task :next p1:Place p2:Place :Transition :inps :outs L R=NAC
  • 23.
    23 BINDING Concept binding: • Bindeach element in the concept to an element in the meta-model. Task SETask name  id initial  isInitial next  following Binding SETaskEngineer Coding Testing actors * Software Engineering processes Design SimpleTask concept Task name: String initial: boolean next * Analysis id: String isInitial: boolean following 0..5
  • 24.
    ADAPTATION The transformation getsautomatically adapted Similar to template instantiation in generic programming 24 t:SETask id=n t:SETask id=n :Place id=n tokens=t.isInitial?1:0 t1:SETask t2:SETask :following p1:Place p2:Place t1:SETask t2:SETask :following p1:Place p2:Place :Transition :inps :outs
  • 25.
    IS THE RESULTING TRANSFORMATIONCORRECT? Yes, by following some rules: Rules for the binding • Cardinalities • Composition, features, subtyping, etc Depends on what you do with the concept • Read only (e.g., for M2M transformation) • Write (e.g., for in-place transformation) 25 SETaskTask SETaskTask Concept Meta-modelbinding R W Sánchez Cuadrado, Guerra, de Lara. “Flexible Model-to-Model Transformation Templates: An Application to ATL”. JOT 2012. Task next * SETask R following 0..5 SETask following 0..5 Task next * W
  • 26.
    26 MORE FLEXIBLE BINDINGS Concepts expressa particular design choice on how structure is organized Different meta-models may implement the same structure differently • Association as intermediate class • Enumerate as subclases • etc. binding Task name: String initial: boolean next * Activity ident: String InitialActivity FlowEdge from to
  • 27.
    27 BINDING ADAPTERS Elements inthe concept are variables • Allow binding them to expressions: adapters Adapters resolve the heterogeneities between the concept and the meta-model They induce an adaptation of the transformation Sánchez Cuadrado, Guerra, de Lara. “A Component Model for Model Transformations”. IEEE Trans. Software Eng. 2014. de Lara, Guerra. “Towards the flexible reuse of model transformations: A formal approach based on graph transformation”. JLAMP 83(5-6). 427-458 (2014)
  • 28.
    rule Task2Place { fromt : Pr!Task to p : PN!Place ( tokens <- if t.initial then 1 else 0 endif )} 28 Task initial: boolean next * Concept Activity InitialActivity Flow Edge from to Task  Activity Task.initial  if (self.oclIsKindOf(InitialActivity)) then true else false endif Task.next  FlowEdge.allInstances()-> select(e|e.from=self)-> collect(to) Meta-model typed on helper context Pr!Activity def: initial : Boolean = if (…) then true else false endif; rule Task2Place { from t : Pr!Activity to p : PN!Place ( tokens <- if t.initial then 1 else 0 endif )} typed on adaptation
  • 29.
    ON THE ROLEOF CONCEPTS… Aren’t concepts characterizing a set of “suitable” meta-models for the transformation? Isn’t that exactly what meta-models do (for models)? Concepts as meta-meta-models Rely on typing: no need to adapt the transformation 29
  • 30.
  • 31.
    Language families asinstances of a common meta-model MULTI-LEVEL BASED REUSE 31 Process Modelling Software Process Modelling Language Educational Modelling Language Software Process Models Educational Models de Lara, Guerra, Sánchez Cuadrado. “Model-driven engineering with domain specific meta-modelling languages”. SoSyM 2015 (Springer).
  • 32.
    32 MULTI-LEVEL BASED GENERICITY Transformations canbe defined at the top meta-level Applicable to instances of all languages in the family Process Modelling Software Process Modelling Language Educational Modelling Language Software Process Models Educational Models … Transformation defined on applicableto
  • 33.
    33 MULTI-LEVEL BASED GENERICITY Task Kind Gateway Kind Seq Par src tar * * Test:TaskKindCoding:TaskKind T2C: Seq C2T: Seq src tar srctar data: Coding unitDt: Test gui: Coding : C2T src tar : T2C tar src «conf» «conf» data: Coding unitDT: Test gui: Coding rule Task2Place transform t : Level0!TaskKind to p : Place { ... } .... defined on
  • 34.
    rule Task2Place transform t: Level0!TaskKind to p : Place { ... } .... 34 MULTI-LEVEL BASED GENERICITY data: Coding unitDT: Test gui: Coding rule Coding2Place transform t : Coding to p : Place {…} rule Test2Place transform t : Test to p : Place {…} defined on data: Coding unitDt: Test gui: Coding : C2T src tar : T2C tar src «conf» Task Kind Gateway Kind Seq Par src tar * * Test: TaskKindCoding:TaskKind T2C: Seq C2T: Seq src tar srctar «conf»
  • 35.
    35 MULTI-LEVEL VS CONCEPT- BASEDGENERICITY Concept-based Genericity Multi-level-based Genericity • A posteriori binding: meta- models may exist first. • Adapters and hybrid concepts to solve heterogeneities. • A priori: meta-meta-model should exist first. • Ability to apply an operation several meta-levels below. • Domain-Specific meta- modelling.
  • 36.
    36 CAN WE GETTHE BEST OF BOTH APPROACHES? • A posteriori binding: meta-models may exist first. • Adapters or Hybrid concepts to solve heterogeneities. • Independence of the transformation language Concept-based reuse Multi-level reuse
  • 37.
    37 CAN WE GETTHE BEST OF BOTH APPROACHES? • A posteriori binding: meta-models may exist first. • Adapters to solve heterogeneities. • Independence of the transformation language Concept-based reuse Multi-level reuse Let’s make the typing relation: • A-posteriori • As flexible as adapters
  • 38.
  • 39.
    A-POSTERIORI TYPING 39 A moreflexible typing mechanism for MDE Decouple instantiation from classification • Interfaces in object-oriented programming • Roles in role-based programming languages Allow dynamic typing and multiple classifiers for objects Type and instance-level reclassification specifications de Lara, Guerra. A Posteriori Typing for Model-Driven Engineering: Concepts, Analysis, and Applications. ACM Trans. Softw. Eng. Methodol. 25 (4) 31:1-31:60 (2017)
  • 40.
    CONSTRUCTIVE VS A-POSTERIORI TYPING 40 Task start:Date duration: int name: String review: Task start= 8/5/15 duration= 30 name= “rev” creation «instance of» Measurable quantity: int Schedulable date: Date review: Task Tasks meta-model model Scheduling MM Measuring MM model «instance of» «instance of» start= 8/5/15 duration= 30 name= “rev” «Schedulable,Measurable» Constructive typing A-posteriori typing Constructive typing A-posteriori typing
  • 41.
    SPECIFYING AP TYPINGS ATTHE TYPE-LEVEL 41 Schedulable date: Date span: double review: Task Tasks meta-model ( MMC ) start= 8/5/15 /months= 1 duration= 30 name= “rev” Scheduling MM ( MMR ) Task start: Date duration: int name: String Typing Specification Task  Schedulable self.start  date /months: double=self.duration/30  span «Schedulable» «date» «span» creation «instance of» (constructive) «instance of» (a-posteriori) • Derived attributes (months) defined in the typing specification. • Typing rules: ensure syntactic correctness
  • 42.
    TYPE-LEVEL AP TYPING 42 date:Date span: double writing: Task start= 15/3/15 duration= 90 name= “wrt” review: Task start= 8/5/15 duration= 30 name= “rev” Schedulable Scheduling MM (MMR) Instance model of Tasks ( MMC ) «instance of» (a-posteriori) “Scheduling glasses” Operations review: Schedulable date= 8/5/15 span= 1 {review, writing} {1, 3} Schedulable.allInstances() writing: Schedulable date= 15/3/15 span= 3 Schedulable.allInstances()-> collect(span)
  • 43.
    SPECIFYING AP TYPINGSAT THE INSTANCE LEVEL 43 date: Date span: double writing: Task start= 15/3/15 duration= 90 name= “wrt” review: Task start= 8/5/15 /months= 1 duration= 30 name= “rev” «Schedulable» «date» «instance of» (a-posteriori) Schedulable Scheduling MM (MMR) Typing Specification Task.allInstances()->select(duration<80)  Schedulable self.start  date /months: double=self.duration/30  span Instance model of Tasks ( MMC ) «span» • Typing defined by queries. • Derived attributes defined by queries as well.
  • 44.
    INSTANCE-LEVEL AP TYPING 44 Operations date:Date span: double writing: Task start= 15/3/15 duration= 90 name= “wrt” review: Task start= 8/5/15 duration= 30 name= “rev” Schedulable Scheduling MM (MMR) Instance model of Tasks ( MMC ) «instance of» (a-posteriori) review: Schedulable date= 8/5/15 span= 1 Schedulable.allInstances()-> collect(span) {review} {1} Schedulable.allInstances() “Scheduling glasses”
  • 45.
    INSTANCE-LEVEL AP TYPING 45 Operations date:Date span: double writing: Task start= 15/3/15 duration= 60 name= “wrt” review: Task start= 8/5/15 duration= 30 name= “rev” Schedulable Scheduling MM (MMR) Instance model of Tasks ( MMC ) «instance of» (a-posteriori) review: Schedulable date= 8/5/15 span= 1 Schedulable.allInstances()-> collect(span) {review, writing} {1, 2} Schedulable.allInstances() “Scheduling glasses” writing: Schedulable date= 15/3/15 span= 2
  • 46.
    Activity  Task ident name /init : boolean = self.oclsKindOf(InitialActivity)  initial /follow: Task[*] = FlowEdge.allInstances()-> select(e|e.from=self)-> collect(e|e.to)  next EXAMPLE 46 Task name: String initial: boolean next * Activity ident: String InitialActivity FlowEdge from to
  • 47.
    A-POSTERIORI TYPING ASA TRANSFORMATION 47 Task name: String initial: boolean next Place Transitionid: String tokens: int inps outs * * task2PN Task.allInstances()  Place name  id /toks : int = if (self.initial) then 1 else 0  tokens Task.allInstances()->select(t|t.next.isDefined())  Transition /prevs : Task[*] = Set{self}  inps /nexts : Task[*] = Set{self.next}  outs 0..1
  • 48.
    A-POSTERIORI TYPING ASA TRANSFORMATION 48 Place Transitionid: String tokens: int inps outs * * t1:Task name=“t1” initial=false name=“t2” initial=true :next name=“t3” initial=false :next t2:Task t3:Task Task name: String initial: boolean next 0..1 task2PN
  • 49.
    A-POSTERIORI TYPING ASA TRANSFORMATION 49 Place Transitionid: String tokens: int inps outs * * «Place» «Place, Transition» «Place, Transition» «id» «id» «id» Task name: String initial: boolean next 0..1 task2PN t1:Task name=“t1” initial=false name=“t2” initial=true :next name=“t3” initial=false :next t2:Task t3:Task
  • 50.
    A-POSTERIORI TYPING ASA TRANSFORMATION 50 Place Transitionid: String tokens: int inps outs * * t2:Task t3:Task «Transition» «Transition» «outs» «outs» «inps» «inps» Task name: String initial: boolean next 0..1 task2PN «Place» «Place» «id» «id»name=“t2” initial=true name=“t3” initial=false t2:Task t3:Task «Place» «id» t1:Task name=“t1” initial=false
  • 51.
  • 52.
    BENTŌ: CONCEPT-BASED REUSE FORATL Adaptation of ATL transformations according to the binding Support for refactoring meta-models into concepts 52 Sánchez Cuadrado, Guerra, de Lara. “A Component Model for Model Transformations”. IEEE TSE 2014 Sánchez Cuadrado, Guerra, de Lara. “Reusable Model Transformation Components with bentō”. ICMT’ 15
  • 53.
    // Model Tasks someTasks{ Task t0 { start = "30/04/2015"; duration = 30; name = "coding"; } } METADEPTH 53 Multi-level textual modelling Concepts • Over every Epsilon language • Hybrid concepts A-posteriori typing • Over every Epsilon language Analysis of type-level specs • Integration with the USE validator • Bidirectional reclassification • Reclassification totality and surjectivity // Meta-model Model Tasks { Node Task { start : Date; duration : int; name : String; } }
  • 54.
    CONCEPT-BASED REUSE 54 concept SimpleTasks(&M, &T, &initial) { Model &M{ Node &T { &initial : boolean; name : String; } } } bind SimpleTasks( SEProcess, SEProcess::SETask, SEProcess::SETask.isInitial ) <<some operation>> rule Task2Place transform task : Source!&T to place : Target!Place { place.name := task.name; if (task.&initial=true) place.tokens:=1 else place.tokens:=0 } ETL transformation
  • 55.
    55 Model ProcessModel@2 { NodeTask { name@1 : String[0..1]; initial : boolean = false; } } ProcessModel SoftwareProcess { Task Analysis { name = “reqs, analysis”; } } SoftwareProcess aSoftProcess{ Analysis a { initial = true; } } @model(potency=0) rule Task2Place transform task : Source!Task to place : Target!Place { place.name := task.name; if (task.initial=true) place.tokens:=1 else place.tokens:=0 } ETL transformation MULTI-LEVEL BASED REUSE
  • 56.
    56 type Factory PetriNet{ Conveyor::parts > Place::tokens, Generator::outps > Transition::outs, Terminator::inps > Transition::ins, Assembler::inps > Transition::ins, Assembler::outps > Transition::outs, } A-POSTERIORI TYPINGS
  • 57.
    Transformations defined overPetri nets can be reused “as is” for Factory • Provide an AP typing from Factory to Petri nets • Factory models become typed as Petri nets 57 // EOL excerpt of the simulator operation Transition enabled() : Boolean { return self.ins.forAll(p| p.tokens.size()>0); } operation step() : Boolean { var enabled : Set(Transition) := Transition.all.select( t | t.enabled()); ... // fire one random Transition from enabled } A-POSTERIORI TYPINGS
  • 58.
    CONCLUSIONS 58 Lower entry-level toMDE to make it more widely used Approaches to reuse model transformations in MDE • Concept-based • Ideas from generic programming • Automates ad-hoc reuse • Multi-level modelling • Families of languages • Independent of transformation language • A-posteriori typing • Provide additional types via retyping specifications • Improve model-adaptation based reuse
  • 59.
    FUTURE WORK 59 Product-Lines basedreuse • With Salay, Chechik (UoT) • Analysis Requirement models as the interface of transformations • More powerful than concepts Exploit a-posteriori typing in MDE • Multiple typings for MDE • Retypings as bx transformations Unified approach?
  • 60.