Parsing Non‐LR(<i>k</i>) grammars with yacc

Merrill, Gary H.

doi:10.1002/spe.4380230803

Cited by 8 publications

(5 citation statements)

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“…Rather than hand-write a parser, many choose to use an existing parsing tool. However, these tools are known for their maintenance and extension challenges, vague error descriptions, and frustrating shift/reduce and reduce/reduce conflicts (Merrill 1993).…”

Section: Introductionmentioning

confidence: 99%

On the complexity and performance of parsing with derivatives

2016

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Current algorithms for context-free parsing inflict a tradeoff between ease of understanding, ease of implementation, theoretical complexity, and practical performance. No algorithm achieves all of these properties simultaneously. Might et al. introduced parsing with derivatives, which handles arbitrary context-free grammars while being both easy to understand and simple to implement. Despite much initial enthusiasm and a multitude of independent implementations, its worst-case complexity has never been proven to be better than exponential. In fact, high-level arguments claiming it is fundamentally exponential have been advanced and even accepted as part of the folklore. Performance ended up being sluggish in practice, and this sluggishness was taken as informal evidence of exponentiality. In this paper, we reexamine the performance of parsing with derivatives. We have discovered that it is not exponential but, in fact, cubic. Moreover, simple (though perhaps not obvious) modifications to the implementation by Might et al. lead to an implementation that is not only easy to understand but also highly performant in practice.

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Section: Introductionmentioning

confidence: 99%

On the complexity and performance of parsing with derivatives

2016

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“…The parsing process can be considered a text-to-model (T2M) transformation. In practice, the parser is usually generated from the grammar by a parser generator, like ANTLR [24,25], Bison [26], or Yacc [27,28]. The inverse of the T2M transformation is the model-to-text (M2T) transformation [29,30].…”

Section: Processes and Artifacts In Text-based Modelingmentioning

confidence: 99%

A Survey on Text-based Modeling in Model Evolution and Management

Somogyi¹,

Asztalos²

2019

Period. Polytech. Elec. Eng. Comp. Sci.

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Model-driven software engineering methodologies like model-driven engineering aim to improve the productivity of software development by using graph-based models as the main artifacts during development, and generating the source code from these models. The models are usually displayed and edited using a graphical notation. However, they can also be described using a textual notation. This has some advantages and disadvantages compared to the graphical approach. For example, while editing the model, we can better focus on the details instead of a broad overview. Similarly to source code, models evolve rapidly during development. Handling and managing the evolution of models is an important task in model-driven methodologies and is an active research area today. However, there exist few research on text-based modeling approaches, compared to graph-based ones. This paper introduces the text-based modeling research field based on existing literature, and presents the state-of-the-art of the field related to model evolution and management. Our goal is to identify challenges and directions for future research in this field. The main topics covered are model differencing and merging, and the synchronization of the textual and graphical notations.

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“…Merrill [9] Regarding top-down parsers, which are used to parse type of grammar called LL, a set of parser generators are used such as Coco/R [11], ANTLR 3 [12], ANTLR 4 [13], and JavaCC [14]. They require the grammar to be LL(k).…”

Section: Fig 1: Using Conditional and Unconditional Actions Bymentioning

confidence: 99%

“…[7,8,9,10] are some attempts to use backtracking. The backtracking approach suggests that when finding a non-deterministic rule, then a trial parsing is done to the first alternative.…”

Section: Introductionmentioning

confidence: 99%

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An Implementation of a Fast Threaded Nondeterministic LL(*) Parser Generator

AbdelLatif¹,

Kamel²,

Bahgat³

2015

IJCA

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Parsers are used in many applications such as compilers, NLP and other applications. Parsers that are developed by hand are a complex task and require a generator to automatically generate the parser. The generator reads a grammar and generates a fully working parser. This paper proposes separating the semantic actions' execution from the parsing phase. The parser generates a queue of semantic actions attached with grammar rules to be visited in case of successful parsing. By this separation, the execution time of the parsing phase can be enhanced. More importantly, this will avoid the incorrect execution of semantic actions when dealing with non-deterministic grammars. Investigating an implementation for the parallelization of the parsing phase for non-deterministic rules is also another contribution of the paper. A previous theoretical work of this paper was made in General TermsCompiler, parsers and parser generator

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Parsing Non‐LR(k) grammars with yacc

Cited by 8 publications

References 0 publications

On the complexity and performance of parsing with derivatives

On the complexity and performance of parsing with derivatives

A Survey on Text-based Modeling in Model Evolution and Management

An Implementation of a Fast Threaded Nondeterministic LL(*) Parser Generator

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