Rascal: A DSL for SCAM · The SCAM domain Uses common... Data-structures Algorithms Nothing new!...

Rascal: A DSL for SCAMJurgen Vinju

Tijs van der StormPaul Klint

Amsterdam, The Netherlands

Hall of fameBob Fuhrer

Emilie BallandArnold Lankamp

Bas Basten

Tuesday, September 29, 2009

The complexity of bridging an analysis tool to a transformation tool shadows the complexity of the analyses and transformations themselves

[Vinju & Cordy Dagstuhl 2005]


Every SCAM tool requires both “analysis” and “transformation”

features



features

Refactoring



features

RefactoringSlicing



features

RefactoringSlicing

Reverse Engineering



features

RefactoringSlicing

Reverse Engineering Compilation



features

RefactoringSlicing


Restructuring



features

RefactoringSlicing


Restructuring

Static checking


Most language parametric SCAM tools are geared towards

either “analysis” or “transformation”




TXL




TXLStrategoXT




TXLStrategoXT

Crocopat




TXLStrategoXT

Crocopat GROK




TXLStrategoXT

Crocopat GROK

ASF+SDF




TXLStrategoXT

Crocopat GROK

ASF+SDF

RScript


Many great SCAM tools are implemented in a GPL


Many great SCAM tools are implemented in a GPL

A GPL allows fine-grained integration between

analysis and transformation


A DSL for SCAM?

That covers SCAM

That scales down and scales up

That is easier


The SCAM domain

Source Code

Models

Pictures

GenerationExtraction

FormalizationVisualization

Transformation

Conversion

Analysis


The SCAM domainUses common...

Data-structures

Algorithms

Nothing new! But still..

Synthesize into a DSL

Conceptually

Syntactically

Semantically

Source Code

Models

Pictures



Transformation

Conversion

Analysis


The SCAM domainUses common...

Data-structures

Algorithms

Nothing new! But still..

Synthesize into a DSL

Conceptually

Syntactically

Semantically

Source Code

Models

Pictures



Transformation

Conversion

Analysis Pattern Matching


Rascal has to scale down to make simple tasks short and easy to

experiment with



experiment with

Plain old REPL, no static type

errors



experiment with


errors

First regexps, then CFG’s



experiment with


errors


Complete & domain specific, expression language: visit, pattern matching,

relational calculus



experiment with


errors


Complete & domain specific, expression language: visit, pattern matching,

relational calculus

URI literals: files,

projects, SVN, ...


Rascal has to scale up to complex analysis and transformation algorithms that employ reuse of

(library) functionality and allow design for maintainability




Modules are statically

checked





checked

Immutable data





checked

Immutable dataCo-variant sub-types





checked

Immutable data h.o. parametric polymorphism, rank-1 and 2

Co-variant sub-types





checked

Immutable data

Efficient and fully typed

(de)serialization

h.o. parametric polymorphism, rank-1 and 2





Function-local type inference


checked

Immutable data


(de)serialization






lexically scoped

backtracking



checked

Immutable data


(de)serialization






lexically scoped

backtracking



checked

Immutable data


(de)serialization



blaConcrete syntaxbla

bla

IDE support bla

blaVisualization support

bla


We have implemented Infer generic type arguments [Fuhrer et al. ECOOP2005]

on Generic Featherweight Java [Igarashi et al. TOPLAS2002]

The size of the Rascal code is equal to thesize of the formal definitions in the papers


Rascal

Analysis and manipulation are happily married.

We should think of them as one.


Rascal

Analysis and manipulation are happily married.

We should think of them as one.

http://www.meta-environment.org




Rascal: A DSL for SCAM · The SCAM domain Uses common... Data-structures Algorithms Nothing new!...

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