Efficient annotated terms

Brand, Mark van den; Jong, H. A. de; Klint, Paul; Olivier, P.A.S.

doi:10.1002/(sici)1097-024x(200003)30:3<259::aid-spe298>3.0.co;2-y

Cited by 138 publications

(64 citation statements)

References 20 publications

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“…One constraining factor is currently the ATerm library used to represent trees, which forms an integral part of Stratego. It is optimized for a maximally shared representation of terms, where identical subtrees occupy the same space in memory [7]. This makes it less suitable for storing additional, dynamic information in tree nodes, in our case parent references (for id.parent) and memoized attribute values.…”

Section: Methodsmentioning

confidence: 99%

Decorated Attribute Grammars: Attribute Evaluation Meets Strategic Programming

Kats

Sloane

Visser

2009

Lecture Notes in Computer Science

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Abstract. Attribute grammars are a powerful specification formalism for treebased computation, particularly for software language processing. Various extensions have been proposed to abstract over common patterns in attribute grammar specifications. These include various forms of copy rules to support non-local dependencies, collection attributes, and expressing dependencies that are evaluated to a fixed point. Rather than implementing extensions natively in an attribute evaluator, we propose attribute decorators that describe an abstract evaluation mechanism for attributes, making it possible to provide such extensions as part of a library of decorators. Inspired by strategic programming, decorators are specified using generic traversal operators. To demonstrate their effectiveness, we describe how to employ decorators in name, type, and flow analysis.

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Section: Methodsmentioning

confidence: 99%

Decorated Attribute Grammars: Attribute Evaluation Meets Strategic Programming

Kats

Sloane

Visser

2009

Lecture Notes in Computer Science

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“…This nature allows them to be efficiently stored using maximal sharing, ensuring a significant decrease in memory usage [9]. Based on maximal sharing, all identical subtrees occupy the same space in memory, allowing constant-time equality tests between branches using pointers.…”

Section: Representation Of Modelsmentioning

confidence: 99%

Code generation by model transformation: a case study in transformation modularity

et al. 2009

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The realization of model-driven software development requires effective techniques for implementing code generators for domain-specific languages. This paper identifies techniques for improving separation of concerns in the implementation of generators. The core technique is code generation by model transformation, that is, the generation of a structured representation (model) of the target program instead of plain text. This approach enables the transformation of code after generation, which in turn enables the extension of the target language with features that allow better modularity in code generation rules. The technique can also be applied to 'internal code generation' for the translation of high-level extensions of a DSL to lower-level constructs within the same DSL using model-to-model transformations. This paper refines our earlier description of code generation by model transformation with an improved architecture for the composition of model-to-model normalization rules, solving the problem of combining type analysis and transformation. Instead of coarse-grained stages that alternate between normalization and type analysis, we have developed a new style of type analysis that can be integrated with normalizing transformations in a fine-grained manner. The normalization strategy has a simple extension interface and integrates non-local, context-sensitive transformation rules. We have applied the techniques in a realistic case study of domain-specific language engineering, i.e. the code generator for WebDSL, using Stratego, a high-level transformation language that integrates model-to-model, modelto-code, and code-to-code transformations.

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“…If there are alternative derivations, they are collected under an ambiguity node. We use maximally shared ATerms [6] to represent parse trees. ATerms are directed acyclic graphs, which prohibits by definition the construction of cycles.…”

Section: Construction Of -Treesmentioning

confidence: 99%

Faster Scannerless GLR Parsing

Economopoulos

Klint

Vinju

2009

Lecture Notes in Computer Science

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Abstract. Analysis and renovation of large software portfolios requires syntax analysis of multiple, usually embedded, languages and this is beyond the capabilities of many standard parsing techniques. The traditional separation between lexer and parser falls short due to the limitations of tokenization based on regular expressions when handling multiple lexical grammars. In such cases scannerless parsing provides a viable solution. It uses the power of context-free grammars to be able to deal with a wide variety of issues in parsing lexical syntax. However, it comes at the price of less efficiency. The structure of tokens is obtained using a more powerful but more time and memory intensive parsing algorithm. Scannerless grammars are also more non-deterministic than their tokenized counterparts, increasing the burden on the parsing algorithm even further. In this paper we investigate the application of the Right-Nulled Generalized LR parsing algorithm (RNGLR) to scannerless parsing. We adapt the Scannerless Generalized LR parsing and filtering algorithm (SGLR) to implement the optimizations of RNGLR. We present an updated parsing and filtering algorithm, called SRNGLR, and analyze its performance in comparison to SGLR on ambiguous grammars for the programming languages C, Java, Python, SASL, and C++. Measurements show that SRNGLR is on average 33% faster than SGLR, but is 95% faster on the highly ambiguous SASL grammar. For the mainstream languages C, C++, Java and Python the average speedup is 16%.

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Efficient annotated terms

Cited by 138 publications

References 20 publications

Decorated Attribute Grammars: Attribute Evaluation Meets Strategic Programming

Decorated Attribute Grammars: Attribute Evaluation Meets Strategic Programming

Code generation by model transformation: a case study in transformation modularity

Faster Scannerless GLR Parsing

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