The design and implementation of Object Grammars

Storm, Tijs van der; Cook, William R.; Loh, Alex

doi:10.1016/j.scico.2014.02.023

Cited by 19 publications

(12 citation statements)

References 44 publications

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“…Bridging grammarware and modelware has received a lot of attention, especially in how to map grammar based formalisms to meta-modeling frameworks, see for instance in [8,13,22]. The use of textual representations of models is generally recognized as being beneficial for productivity and tool development [8].…”

Section: Discussion and Related Workmentioning

confidence: 99%

Model Transformation with Immutable Data

Klint

Storm

2016

Theory and Practice of Model Transformations

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Abstract.Mainstream model transformation tools operate on graph structured models which are described by class-based meta-models. In the traditional grammarware space, transformation tools consume and produce tree structured terms, which are described by some kind of algebraic datatype or grammar. In this paper we explore a functional style of model transformation using Rascal, a meta-programming language, that seamlessly integrates functional programming, flexible static typing, and syntax-based analysis and transformation. We represent metamodels as algebraic data types (ADTs), and models as immutable values conforming to those data types. Our main contributions are (a) REFS a simple encoding and API, to deal with cross references among model elements that are represented as ADTs; (b) a mapping from models to ADTs augmented with REFS; (c) evaluation of our encoding by implementing various well-known model transformations on state machines, meta-models, and activity diagrams. Our approach can be seen as a first step towards making existing techniques and tools from the modelware domain available for reuse within Rascal, and opening up Rascal's transformation capabilities for use in model driven engineering scenarios. Model Transformation with GrammarwareThere are strong analogies between modelware and grammarware, albeit that terminology is mostly disjoint. For example, in modelware, a state machine model can be described by a model described in Ecore and Ecore itself is described using the Ecore meta-model. In grammarware, a C program can be described by a grammar of the C language written in BNF notation and BNF notation itself is described by a BNF grammar. A key difference between these domains is how models are represented. In the modeling domain models and meta-models are represented and processed as mutable graphs while immutable, tree-based, representation prevails in the grammar domain. The focus of this paper is on analyzing and bridging the impedance mismatch between these graph-based and tree-based domains. This can bring various cross fertilization benefits:• The ecosystem of models and modeling tools becomes available for the grammar-based approaches, e.g., EMF 1 (including Ecore 2 and 1 See

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Section: Discussion and Related Workmentioning

confidence: 99%

Model Transformation with Immutable Data

Klint

Storm

2016

Theory and Practice of Model Transformations

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“…TMDIFF's differencing strategy resembles the model merging technique used Ensō [18]. The Ensō "merge" operator also traverses a spanning tree of two models in parallel and matches up object with the same identity.…”

Section: Discussion and Related Workmentioning

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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“…Workbenches Rascal, Spoofax or SugarJ use composing sets of rewrite rules, Ensõ uses Object grammars based on object-oriented principles [11]. Van der Storm et al [39] formulated Object grammars as their framework for language extension that supports composition for language modularity. They define three types of composition: language reuse, language extension, and language mixin.…”

Section: Related Workmentioning

confidence: 99%

Language composition using source code annotations

2016

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In this paper we examine source code annotations from the viewpoint of formal languages -we discuss their abstract syntax, concrete syntax, and semantics, thus showing the correspondence between annotations and formal languages. We propose to consider a set of all annotations and their parameters processed by the same reference implementation (they belong to the same domain) to be called an annotation-based language. The performed analysis also pinpoints a specificity of annotations in comparison with formal languages in general -the binding between annotations and a host language. We elaborate this idea with an analysis of annotations' potential for language composition, in particular for pure embedding. We then show how pure embedding with annotations can be used for language unification, language referencing by extension, and language extension. This work provides a basis for further research in the field of source code annotations in the context of formal languages.

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The design and implementation of Object Grammars

Cited by 19 publications

References 44 publications

Model Transformation with Immutable Data

Model Transformation with Immutable Data

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

Language composition using source code annotations

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