What's Missing in Language Workbenches
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The document discusses 'shadow models,' a functional transformation language that supports incremental execution and integrates with the MPS IDE. It highlights use cases such as model checking and hierarchical flattening for various applications, along with the importance of providing guidance for novice users in modeling tools. Additionally, it addresses the need for different debuggers for various language paradigms and the potential for further development in real-time program execution and communication tools.
What's Missing in Language Workbenches
- 3. What is Shadow Models? Functional transformation language with support for fixpoints Incremental execution upon change of input model Unidirectional, but with first-class support for lifting results Fully integrated into MPS IDE Code: https://github.com/JetBrains/MPS-extensions/tree/master/code/shadowmodels Docs: https://jetbrains.github.io/MPS-extensions/extensions/shadowmodels/
- 4. Mechanics Input Model Shadow Model ... ‚ User edits the input model A delta is propagated into the transformation engine A change on the shadow model is produced This triggers analysis or update of results in Shadow Model Results/messages are lifted back to input level Results are annotated to the input model Mightbestacked
- 5. Current State (March 2019) Still under active development. Primarily by Sascha Lisson. Used for initial use cases at itemis Stuttgart.
- 6. Kf2 Core Interpreter Generator Verifier Sugar DSL1 DSL2 DSL3 ShadowModels • Minimal, Expressive Core • Functional • Reactive • Sugar on top • Interpter, Generator and Verifier below • Realtime-Trafo with Shadow Models
- 9. Example Use Cases • Mapping of system models to model checkers for interactive verification of temporal properties • Flattening of component hierarchies for type checking • Flattening of hierarchical feature models for path expression evaluation • Weaving in of safety concerns into C code. • Desugaring of business DSLs to a core functional language; that core language would then feature an interpreter, a compiler and an integration with an SMT solver
- 10. Use Case: Safety Patterns
- 11. Use Case: Safety Patterns +
- 12. Use Case: Safety Patterns + =
- 13. Use Case: Interactive FM Specialization Klaus Birken
- 14. Use Case: Interactive FM Specialization Klaus Birken Uncheck Hybrid
- 15. Use Case: Interactive FM Specialization Klaus Birken Check Combustion
- 16. Use Case: Interactive FM Specialization Klaus Birken Check RearCamera
- 17. Guidance 2
- 18. Applications Modeling Toolvs. But not like that! Novice Users need Guidance!
- 19. Applications Modeling Toolvs. Novice Users need Guidance! What am I working on? What should I do next? How complete is my model? What is the quality of my model? Roles Tasks, Workflow Model States Metrics Defined jointly with the language.
- 20. Semantics 3
- 21. Semantics Spec Interpreter Compiler Verifier Functional + Imperative K + FunCons Must be understandable by an engineer, not a mathematician!
- 23. KernelF Semantics Solver Model Checker Rules Engine Functional Interpreter Reactive Interpreter Several Projects / DSLs Lots of ideas J Grow on other Foundations! Grow on functional KernelF.
- 24. Debuggers 4
- 25. Debuggers Different ones for different language paradigms Functional => Tree Imperative => Stepping Declarative => ?
- 26. Debuggers Different ones for different language paradigms Functional => Tree Imperative => Stepping Declarative => ? Often the (semantic reverse) of generation it has to recover DSL semantics for watches, views, breakpoints Related to simulators and „model animators“ No good tool support for building them! Realtime vs. Post Mortem
- 27. Liveness 5
- 28. Beyond Balls and Trees Not too much has happened since the legandary demo. Overlay program execution data Run tests and illustrate results in the background. Instance-based programming (Jonathan Edwards) That‘s it? What else can we do? Which LWB-support can be build?