The State of the Art in Language Workbenches

Erdweg, Sebastian; Storm, Tijs van der; Völter, Markus; Boersma, M.V.; Bosman, Remi; Cook, William R.; Gerritsen, Albert; Hulshout, Angelo; Kelly, Steven; Loh, Alex; Konat, Gabriël D. P.; Molina, Pedro J.; Palatnik, Martin; Pohjonen, Risto; Schindler, Eugen; Schindler, Klemens; Solmi, Riccardo; Vergu, Vlad; Visser, Eelco; Vlist, Kevin van der; Wachsmuth, Guido; Woning, Jimi van der

doi:10.1007/978-3-319-02654-1_11

Cited by 129 publications

(112 citation statements)

References 49 publications

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“…The composition extends the delegator visitor CDWithAutomatonDelegatorVisitor (l. 2) that provides statically typed methods for the composition (used in ll. [4][5]. Fig.…”

Section: Composing Concrete Visitors During Language Embeddingmentioning

confidence: 98%

“…For concrete class nodes, we also generate a traverse method for subtree traversal. All methods have default implementations 4 and hence do not require an implementation. Instead, they are best understood as hook points.…”

Section: Generating the Visitor Pattern As Dsl Infrastructurementioning

confidence: 99%

“…Many language workbenches (LWBs) employ context-free grammars (CFGs) to describe modeling languages [4]. From these grammars, LWBs derive the abstract syntax of languages in form of abstract syntax trees (ASTs).…”

Section: Introductionmentioning

confidence: 99%

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Compositional Language Engineering Using Generated, Extensible, Static Type-Safe Visitors

Heim

Nazari

Rumpe

et al. 2016

Modelling Foundations and Applications

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Abstract. Language workbenches usually produce infrastructure to represent models as abstract syntax trees (AST) and employ processing infrastructure largely based on visitors. The visitor pattern suffers from the expression problem regarding extensibility and reuse. Current approaches either forsake static type safety, require features unavailable in popular object-oriented languages (e.g., open classes), or rely on procedural abstraction and thereby give up the object-oriented data encapsulation (the AST) itself. Our approach to visitors exploits knowledge about the AST and generation of statically type-safe external visitor interfaces that support extensibility in two dimensions: (1) defining new operations by implementing the interface and (2) extending the underlying data structure, usually without requiring adaptation of existing implemented visitors. We present a concept of visitor development for language engineering that enables an adaptable traversal and provides hook points for implementing concrete visitors. This approach is applicable to single DSLs and to language composition. It thus enables a transparent, easy to use, and static type-safe solution for the typical use cases of language processing.

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“…The composition extends the delegator visitor CDWithAutomatonDelegatorVisitor (l. 2) that provides statically typed methods for the composition (used in ll. [4][5]. Fig.…”

Section: Composing Concrete Visitors During Language Embeddingmentioning

confidence: 98%

Section: Generating the Visitor Pattern As Dsl Infrastructurementioning

confidence: 99%

See 1 more Smart Citation

Compositional Language Engineering Using Generated, Extensible, Static Type-Safe Visitors

Heim

Nazari

Rumpe

et al. 2016

Modelling Foundations and Applications

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show abstract

“…These labels capture source locations of names. That is, a name occurrence is identified with its line and column number and/or character offset 4 . Since identifiers can never overlap, labels are guaranteed to be unique, and the actual name corresponding to label can be easily retrieved from the source text itself.…”

Section: Overviewmentioning

confidence: 99%

“…Since RASCAL is a textual language workbench [4] all models are represented as text, and then parsed into an abstract syntax tree (AST). Except for primitive values (string, boolean, integer etc.…”

Section: Implementation In Rascalmentioning

confidence: 99%

Origin Tracking $$+$$ Text Differencing $$=$$ Textual Model Differencing

Rozen

Storm

2015

Theory and Practice of Model Transformations

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Abstract. In textual modeling, models are created through an intermediate parsing step which maps textual representations to abstract model structures. Therefore, the identify of elements is not stable across different versions of the same model. Existing model differencing algorithms, therefore, cannot be applied directly because they need to identify model elements across versions. In this paper we present Textual Model Diff (TMDIFF), a technique to support model differencing for textual languages. TMDIFF requires origin tracking during text-to-model mapping to trace model elements back to the symbolic names that define them in the textual representation. Based on textual alignment of those names, TMDIFF can then determine which elements are the same across revisions, and which are added or removed. As a result, TMDIFF brings the benefits of model differencing to textual languages.

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Fusing Modeling and Programming into Language-Oriented Programming

Voelter¹

2018

Leveraging Applications of Formal Methods, Verification and Validation. Modeling

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The State of the Art in Language Workbenches

Cited by 129 publications

References 49 publications

Compositional Language Engineering Using Generated, Extensible, Static Type-Safe Visitors

Compositional Language Engineering Using Generated, Extensible, Static Type-Safe Visitors

Origin Tracking $$+$$ Text Differencing $$=$$ Textual Model Differencing

Fusing Modeling and Programming into Language-Oriented Programming

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