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McGill University Proposal Exam School of Computer Science Ph.D. Candidate in the Modelling, Simulation and Design Lab Eugene Syriani.

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Presentation on theme: "McGill University Proposal Exam School of Computer Science Ph.D. Candidate in the Modelling, Simulation and Design Lab Eugene Syriani."— Presentation transcript:

1 McGill University Proposal Exam School of Computer Science Ph.D. Candidate in the Modelling, Simulation and Design Lab Eugene Syriani

2 Proposal Exam OUTLINE  Context  Thesis  Overview of the Approach  Planning  Conclusion 2

3 Proposal Exam MODEL-DRIVEN ENGINEERING 3 Model Wheel Transmission Mechanics of engine Electric circuits Security Speed control Resistance to snow System Meta-Model represented by conforms to

4 Proposal Exam MULTI-PARADIGM MODELLING (MPM) Multi-formalism – Domain-specific formalisms Multi-abstraction Meta-Modelling Model Transformation Model everything – Explicitly – At the most appropriate level of abstraction – Using the most appropriate formalism 4

5 Proposal Exam MODEL TRANSFORMATION Manipulate Manipulate: access & modify operations Simulate Simulate: execution Generatecode Generate code: compilation Translate Translate: into other models 5 M1M1 M3M3 M2M2

6 Proposal Exam MODEL TRANSFORMATION DEVELOPMENT Meta-Model of domain 6

7 Proposal Exam MODEL TRANSFORMATION DEVELOPMENT Generate Modelling Environment 7

8 Proposal Exam MODEL TRANSFORMATION DEVELOPMENT Transformation Specification 8 1 4 2 5pac Link 3 ghostLink 1 2 3 1: return self.LHS.nodeWithLabel(1).score + 1 1 2 3 1 2 3 5 4 pacLink foodLink 6 4 pacLink 4 12 3 6 gridLeft ghostLink 12 3 4 7 gridLeft ghostLink 4 12 5 gridRight pacLink 12 4 6 gridRight pacLink 3 3 1 7 8 ghostLink

9 Proposal Exam MODEL TRANSFORMATION DEVELOPMENT Given input model Run transformation – Rules – Unordered, Priority, Layer, Control Flow Output – New model – Modified model Execution 9

10 Proposal Exam PROBLEM STATEMENT Meta-Modelling: well established – Language for model specification – Automatic generation of modelling environments Focus on transformations – Robust theoretical foundation (e.g., graph transformation) – Plethora of model transformation languages (MTL) AGG, ATL, AToM 3, FUJABA, GReAT, MOFLON, ProGreS, QVT, VMTS, VIATRA2,... – Each one provides tremendous value for its domain of expertise  No interoperability  Implementation of transformation paradigm is hard-coded 10

11 Proposal Exam MY THESIS Contribute to the engineering of model transformation languages – At the foundation level – Following MPM principles Model everything: – syntax of MTL – semantics of MTL Provide a framework for building MTLs Design & implement a new MTL, following MPM principles – Core algorithms – Language building blocks – Formalism Focusing on expressiveness of MTL 11

12 Proposal Exam SOLUTION 12

13 Proposal Exam EXPLICIT MODELLING OF TRANSFORMATIONS Consider MTLs as domain-specific languages Explicitly model the patterns & the scheduling 13 Pre-condition Pattern Post-condition Pattern

14 Proposal Exam MODELLING THE MTL 14

15 Proposal Exam RAM PROCESS (quasi-)Automatically generated environment for pattern language 15 Input Meta-ModelOutput Meta-Model Relax Augment Modify Customized Pattern Meta-Model

16 Proposal Exam TRANSFORMATION SPECIFICATION Domain-Specific Transformation Patterns 16

17 Proposal Exam MINIMAL TRANSFORMATION CORE patterns Pre-/post- patterns Matching Matching Rewriting Rewriting Validation Validation of consistent rule application Matches manipulation – Iteration – Roll-back Control flow – Choice – Concurrency Composition Composition Common representation Common representation Features that allow the execution of MTL 17

18 Proposal Exam T-CORE Executable Executable module Efficient Efficient implementation of the Matcher & the Rewriter Combine Combine primitive transformation constructs with “glue language” – Programming – Programming language SBL, Python – Modelling – Modelling language UML Activity Diagrams, DEVS 18

19 Proposal Exam MOTIF-CORE 19 DEVST-Core

20 Proposal Exam MOTIF-CORE 20 DEVS T-Core MoTif-Core

21 Proposal Exam MOTIF 21 Meta-ModelSemantics

22 Proposal Exam TRANSFORMATION EXCEPTION HANDLING Identification & classification Modelling of transformation exceptions Exception handling specification in the MT itself – Post-handling control flow – Propagation mechanism 22

23 Proposal Exam MOTIF FRAMEWORK 23

24 Proposal Exam PLANNING 24

25 Proposal Exam WHAT IS REMAINING? 1.RAM process – Evaluate usability of a completely modelled environment for designing model transformation 2.T-Core – Module based on a model-centric virtual machine – Usable with Python & DEVS – Efficient Matcher & Rewriter 3.MoTif-Core – Compiler to DEVS 4.MoTif Framework – Insert in the loop – Support higher-order transformations – Support exception handling Mainly: implementation... 25

26 Proposal Exam WHAT IS REMAINING? 1.CD2RDBMS – Using MoTif 2.AntWorld Simulation – Using T-Core & Python 3.PacMan Game – Using MoTif & extended MoTif-Core 4.Aspect Weaving – Using MoTif... and case studies 26

27 Proposal Exam CONCLUSION Novel approach for designing MTLs Based on MPM principles Three model transformation formalisms – Primitive building blocks (T-Core)  Problem-specific pattern language – Modularly composable, asynchronous, timed transformations (MoTif-Core) – General purpose transformation (MoTif)  Performance analyses  Compare to other model transformation engineering approaches 27

28 Proposal Exam RAM PROCESS Relax 28 (quasi-)Automatically generated environment for pattern language AugmentModify

29 Proposal Exam HIGHER-ORDER TRANSFORMATION SUPPORT model Easy to treat a model transformation as a model transformed Therefore, a transformation can be itself transformed evolution Allows to handle evolution of – Models – Meta-models – Model transformations 29

30 Proposal Exam HIGHER-ORDER TRANSFORMATION SUPPORT 30

31 Proposal Exam S t  (s) (s,0) s s'' s' Y t tyty y1y1 (s)  int (s) X x1x1 txtx t txtx (s,e)  ext ((s,e),x) (s 0,0) s0s0 0 ATOMIC DEVS BEHAVIOUR


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