Over the last couple of years, I have used MPS successfully to build interesting (modeling and programming) languages in a wide variety of domains, targeting both business users and engineers. I’ve used MPS because it is currently the most powerful language workbench, lots of things are good about iz, in particular, its support for a multitude of notations and language modularity. But it is also obvious that MPS is not going to be viable for the medium to long term future; the most obvious reason for this statement is that it is not web/cloud-based. In this keynote, I will quickly recap why and how we have been successful with MPS, and point out how language workbenches could look like in the future; I will outline challenges, opportunities and research problems. I hope to spawn discussions for the remainder of the workshop.

1. Markus Völter
voelter@acm.org
www.voelter.de
@markusvoelter
Envisioning Future
Language Workbenches

2. Where I come from I

3. Precision
Programming
!=
{
Formulas, Rules
Data Structures
Tables
Values
}
Performance
Scalability
Robustness
Deployment
+ Tests

4. All languages shown in this talk
are built with the open source
JetBrains MPS language workbench.

5. MPS Language Workbench
Language Workbench
Open Source, by Jetbrains
Very Expressive
Used for years in industry
Vast Experience

6. + Refactorings, Find Usages, Syntax Coloring, Debugging, ...
MPS Language Workbench

16. MPS: Notational Freedom
100% crucial for acceptance in domain!
Rabbit Hole
Modeling is more than diagrams!

18. MPS: Language Composition
SPLE on Language Level!

19. Language Patterns
New Language
GPL Extension
Existing
Domain Notation
(Informal)
Formalization
Formalized
Language
Reuse GPL incl. Expressions and TS
Add/Embed DS-extensions
Compatible notational style
Reduce to GPL
Analyze Domain to find Abstractions
Define suitable, new notations
Rely on existing behavioral paradigm
Reuse standard expression language
Interpret/Generate to one or more
GPLs
Use existing notation from domain
Clean up and formalize
Generate/Interpret
Often import existing „models“
Language Extension Patterns

20. Language PatternsLanguage Flavours

22. Language Concerns II

23. Realtime
Incremental
Transformations
1

24. 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

25. 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/

26. 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

27. 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

28. Kf2

29. Semantics
2

30. LWBs Semantics

31. LWBs Semantics

32. LWBs Semantics

33. LWBs + Semantics

34. Semantics
Spec
Interpreter
Compiler
Verifier
Functional + Imperative + Reactive
The tools exist: K, Funcons, VDM
IDE
Deployment
Correctness
But there is very limited integration with LWBs.
Exception: Spoofax + Funcons

35. Semantics
Grow on (imperative) C

36. KernelF
Semantics
Functional
Interpreter
Reactive
Interpreter
Several
Projects / DSLs
Grow on Functional Language...

37. KernelF
Semantics
Solver
Model
Checker
Rules
Engine
Functional
Interpreter
Reactive
Interpreter
Several
Projects / DSLs
Lots of ideas J
Grow on Functional Language...
... and other Foundations.

38. Liveness
3

39. 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?

40. Why do people like Excel?

41. Beyond Balls and Trees

42. 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?

43. Debuggers
4

44. Debuggers
Different ones for different language paradigms
Functional => Tree
Imperative => Stepping
Declarative => ?

45. 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

46. Language
Parametrization
& Interfaces
5

47. Language Composition

48. Language Composition
Defining „abstract“ languages with holes
Example: State machines with pluggable expression language
What is the type of those that can be plugged in?
Language Types or Interfaces / Megamodels
Example: How do you descibe a „valid“ expression language?
Does this include execution sematics?

50. Tool Concerns
III

51. Guidance
6

52. Applications

53. Applications Modeling Toolvs.
But not like that!
Novice Users need Guidance!

54. Applications Modeling Toolvs.
Novice Users need Guidance!

55. Applications Modeling Toolvs.
Novice Users need Guidance!
What am I working on? Roles

56. Applications Modeling Toolvs.
Novice Users need Guidance!
What am I working on?
What should I do next?
Roles
Tasks, Workflow

57. Applications Modeling Toolvs.
Novice Users need Guidance!
What am I working on?
What should I do next?
How complete is my model?
Roles
Tasks, Workflow
Model States

58. 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

59. 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.

60. Permissions
7

61. Permissions
Which user groups can see and edit which part of the model?
Can we define views for showing parts or aspects of models.
Proxies for „hidden details“?

62. Collaboration
8

63. Version Control Systems are a problem

64. Version Control Systems are a problem
https://git-man-page-generator.lokaltog.net/
But file-based version control is often not suitable.

65. Google Docs Style Collaboration
Realtime collaboration à la Google Docs
Language/structure-aware
Some level of transaction or isolation
Some version of branching.

66. The cloud
9

67. Cloud Backend
Local vs. „real“
Data Protection!
LSP-style frontend.

68. Browser Frontends

69. Become more App-Like
UI must be less like an IDE.
Things must look slicker!
It must be easier to integrate
with web apps that are
developed without the LWB:
Microservice-style modeling „services“
Integration on common backend and also with non-model apps.

70. Bring in M0
Language DSLs
Language
Model
User Data
M0
M1
M2
M3
Part of the LWB
Defined using the LWB
Should feel like IDE
Should be on the web
Not feel like IDE
Completely outside in generated App/DB

71. Bring in M0
Language DSLs
Language
Model
M3
Part of the LWB
User Data
Bring into joint repository.
Simplified Migration of models.
MetaEdit+ does this.

72. Bring in M0
Language DSLs
Language
Model
User Data
M3
Part of the LWB
Bring into joint repository.
Simplified Migration of models.
MetaEdit+ does this.
User-data also needs (incremental)
analysis, transformation and migration.
Should also scale indefinitely.
Why „externalize“ through generation etc?

73. Scale Down
10

74. Get Rid of IDE feel

75. Make it simpler.
https://alarmingdevelopment.org/
In terms of UI
But also in terms of paradigm.
Is functional or imperative the answer?

76. Make it modular(er).

77. Explain
Communicate
Convince
Educate
BBonus

78. Maybe more important!
!
How do we call
what we do?
How do we
de-risk tools?
What are the
right paradigms?
Computational
Thinking
End-User
programming
Case Studies