The Grammar Tool Box: A Case Study Comparing GLR Parsing Algorithms

Johnstone, Adrian; Scott, Elizabeth; Economopoulos, Giorgios

doi:10.1016/j.entcs.2004.06.008

Cited by 13 publications

(4 citation statements)

References 18 publications

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“…When this is performed on a suitable grammar (i.e. one without embedded recursion) the unrolled NFA generates the IRIA automaton discussed in [15]. We now view Knuth-style LR automata and the RIGLR IRIA as special cases of this general NFA construction function.…”

Section: The Interplay Between Theory and Practicementioning

confidence: 97%

“…In particular, we have compared Tomita's original algorithm, and Farshi's modification of it, with the RNGLR algorithm [24,7]; we have compared the Reduction Incorporated GLR algorithm with the other GLR algorithms [23,11]; we have developed resolved right nullable tables and considered their application to RNGLR and LR parsing [22]. We have also made comparative studies of these algorithms when running with a variety of types of LR tables and discussed some of the phenomena that underlie the space and time complexities of these parsers [14,15,13]. More recently we have reported on approaches to the removal of embedded recursion in grammars, a necessary precursor to automaton construction for RIGLR parsers [12,8].…”

Section: Grammars For Standard Programming Languagesmentioning

confidence: 98%

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Proofs and pedagogy; science and systems: The grammar tool box

Johnstone

Scott

2007

Science of Computer Programming

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GTB (the Grammar Tool Box) is the tool that underpins our investigations into generalised parsing. Our goal is to produce a system that supports systematic investigation of various styles of generalised parsing in a way that allows meaningful comparisons between them in a repeatable and easily accessible fashion whilst also allowing: (i) new theoretical ideas to be generated and explored; (ii) production quality parsers to be generated and (iii) humane pedagogy. GTB comprises a language (LC) with various kinds of built-in grammar and automata related objects, and a set of black-box methods written in C++ that provide implementations of grammar transforms, automata construction algorithms, parsing and recognition algorithms, and a variety of visualisation aids. In this paper we focus on the overall rationale for the GTB framework; the GTB design goals; and some detailed operational flows that are supported by GTB.The late Roger Needham famously said that research should be done with a shovel, not tweezers [20], and the computing community both academic and industrial has certainly taken this maxim to heart. However, as archaeologists know, once past the surface layers, using a shovel is dangerous because important details may be obscured; in which case only partial or even incorrect theories may be achievable. In reality, the devil hides in the details.Computing as an academic subject has three main aspects: a purely mathematical one in which machines are seen as the physical realisation of formal results; a scientific one in which the emphasis is on controlled, repeatable experimentation in the pursuit of observable truth; and an engineering facet in which the delivery of cheap, fast and reliable systems is paramount. These three are often in tension with each other, but in all cases the degree to which we document our motivation and design decisions can be critical to wider acceptance of a technology. As in any scientific domain, proofs, scientific observations and engineered artefacts all need their broader context (as well as their core features) explained. Proofs and pedagogy; science and systems: this is what we should be doing.Sadly, computing is not like the established sciences in this respect. We see very few genuinely repeatable studies in the literature, and thus very few repeated experiments, making it hard to distinguish a well-founded consensus view from mere fashion. To be fair, computing does not always lend itself easily to controlled experimentation. The field sits at a nexus between mathematics, engineering, psychology and perhaps sociology. Some of our phenomena are * Corresponding author. Tel.: +44 (0) 1784 443425; fax: +44 (0) 1784 439786.

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Section: The Interplay Between Theory and Practicementioning

confidence: 97%

Section: Grammars For Standard Programming Languagesmentioning

confidence: 98%

Proofs and pedagogy; science and systems: The grammar tool box

Johnstone

Scott

2007

Science of Computer Programming

Self Cite

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“…Among other tools we can cite PAT [13,17], now discontinued, which has been used for the visualization and statistical comparison of various GLR parsers, [18] for visualizing lexical generation processes and [22] that is an educational tool for visualizing compiling techniques based on deterministic parsers.…”

Section: Related Workmentioning

confidence: 99%

Visual exploration of visual parser execution

Costagliola

Rosa

Fuccella

et al. 2021

Multimed Tools Appl

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In this paper, we present ParVis, an interactive visual system for the animated visualization of logged parser trace executions. The system allows a parser implementer to create a visualizer for generated parsers by simply defining a JavaScript module that maps each logged parser instruction into a set of events driving the visual system interface. The result is a set of interacting graphical/text windows that allows users to explore logged parser executions and helps them to have a complete understanding of how the parser behaves during its execution on a given input. We used our system to visualize the behavior of textual as well as visual parsers and describe here two of these uses. Moreover, in order to validate the efficacy of our system, we ran a user experiment where students analyzed a CUP-generated parser both with ParVis, instantiated to LALR parsers, and the standard CUP debug facilities. The results show that students can indeed analyze parser behavior and find mistakes in parser specifications more easily and quickly using ParVis. In particular, in some parser design tasks, using ParVis participants achieved a higher success rate of 50% in 42% less time with respect to the baseline system.

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“…Parser visualization tools either visualize the process of generating a parser from a grammar like LLparse and LRparse [2]. Or they visualize parser execution like PAT [12,15] which has been used for the visualization and statistical comparison of various GLR parsers. Among other tools we can cite [16] for visualizing lexical generation processes and [19] that is an educational tool for visualizing compiling techniques based on deterministic parsers.…”

Section: Related Workmentioning

confidence: 99%

Visualizing Visual Parser Execution

Costagliola

Rosa

Minas

2020

International Conferences on Distributed Multimedia Systems

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Parsing of visual structures like diagrams and graphs is more complicated than parsing strings. This is so because visual structures are inherently more complex than strings, because visual grammars are more difficult to write than string grammars, and because the algorithms for parsing visual structures are usually more complicated than for parsing strings. The developer of a visual parser, therefore, needs more tool support than a developer of a string parser. In fact, developing and debugging a visual parser without proper visualization of the parsing process is very challenging. This paper describes a visualization approach that arose from this need. Its main focus is on the interaction of the developer with the visualization tool in order to explore the execution process of the parser. It has evolved from experiences with developing and debugging parsers by applying different visual parsing methods. In order to better describe it we introduce a concrete example.

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The Grammar Tool Box: A Case Study Comparing GLR Parsing Algorithms

Cited by 13 publications

References 18 publications

Proofs and pedagogy; science and systems: The grammar tool box

Proofs and pedagogy; science and systems: The grammar tool box

Visual exploration of visual parser execution

Visualizing Visual Parser Execution

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