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https://dx.doi.org/10.48550/ar...
Article . 2020
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A semantic framework for PEGs

Authors: Sérgio Queiroz de Medeiros; Carlos Olarte;

A semantic framework for PEGs

Abstract

Parsing Expression Grammars (PEGs) are a recognition-based formalism which allows to describe the syntactical and the lexical elements of a language. The main difference between Context-Free Grammars (CFGs) and PEGs relies on the interpretation of the choice operator: while the CFGs' unordered choice e | e' is interpreted as the union of the languages recognized by e and e, the PEGs' prioritized choice e/e' discards e' if e succeeds. Such subtle, but important difference, changes the language recognized and yields more efficient parsing algorithms. This paper proposes a rewriting logic semantics for PEGs. We start with a rewrite theory giving meaning to the usual constructs in PEGs. Later, we show that cuts, a mechanism for controlling backtracks in PEGs, finds also a natural representation in our framework. We generalize such mechanism, allowing for both local and global cuts with a precise, unified and formal semantics. Hence, our work strives at better understanding and controlling backtracks in parsers for PEGs. The semantics we propose is executable and, besides being a parser with modest efficiency, it can be used as a playground to test different optimization ideas. More importantly, it is a mathematical tool that can be used for different analyses.

Keywords

FOS: Computer and information sciences, Computer Science - Programming Languages, D.3.4, Formal Languages and Automata Theory (cs.FL), [INFO.INFO-SC] Computer Science [cs]/Symbolic Computation [cs.SC], F.4.3, Computer Science - Formal Languages and Automata Theory, F.4.2, Programming Languages (cs.PL), F.4.2; F.4.3; D.3.4

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
Green