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Journal of Information Processing
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A Declarative Extension of Parsing Expression Grammars for Recognizing Most Programming Languages

descriptionPublicationkeyboard_double_arrow_right Article , Preprint 01 Jan 2016Embargo end date: 01 Jan 2015Publisher:Information Processing Society of JapanJournal:Journal of Information Processing, volume 24, pages 256-264 (eissn: 1882-6652, Copyright policy )
Authors: Tetsuro Matsumura; Kimio Kuramitsu;

doi: 10.2197/ipsjjip.24.256 , 10.48550/arxiv.1511.08414

arXiv: http://arxiv.org/abs/1511.08414

A Declarative Extension of Parsing Expression Grammars for Recognizing Most Programming Languages

Abstract

Parsing Expression Grammars are a popular foundation for describing syntax. Unfortunately, several syntax of programming languages are still hard to recognize with pure PEGs. Notorious cases appears: typedef-defined names in C/C++, indentation-based code layout in Python, and HERE document in many scripting languages. To recognize such PEG-hard syntax, we have addressed a declarative extension to PEGs. The "declarative" extension means no programmed semantic actions, which are traditionally used to realize the extended parsing behavior. Nez is our extended PEG language, including symbol tables and conditional parsing. This paper demonstrates that the use of Nez Extensions can realize many practical programming languages, such as C, C\#, Ruby, and Python, which involve PEG-hard syntax.

To appear in Journal of Information Processing, 24(2), 2016

Related Organizations
Keywords

FOS: Computer and information sciences, Computer Science - Programming Languages, Programming Languages (cs.PL)

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citations
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!
2
Average
Average
Average
Green
gold