Evaluating GLR parsing algorithms

Johnstone, Adrian; Scott, Elizabeth; Economopoulos, Giorgios

doi:10.1016/j.scico.2006.04.004

Cited by 12 publications

(3 citation statements)

References 10 publications

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“…This leads to much appreciated linear parse time. It is not true for GLR-type parsing (O(n 3 ) in complexity) and in our case, since we are potentially creating all possible ASTs, we can be exponential [34]. Those parsers create subparsers every time an ambiguity is encountered.…”

Section: Scalabilitymentioning

confidence: 95%

A Reflexive and Automated Approach to Syntactic Pattern Matching in Code Transformations

Lecerf¹,

Brant²,

Goubier³

et al. 2018

2018 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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Empowering software engineers often requires to let them write code transformations. However existing automated or tool-supported approaches force developers to have a detailed knowledge of the internal representation of the underlying tool. While this knowledge is time consuming to master, the syntax of the language, on the other hand, is already well known to developers and can serve as a strong foundation for pattern matching. Pattern languages with metavariables (that is variables holding abstract syntax subtrees once the pattern has been matched) have been used to help programmers define program transformations at the language syntax level. The question raised is then the engineering cost of metavariable support. Our contribution is to show that, with a GLR parser, such patterns with metavariables can be supported by using a form of runtime reflexivity on the parser internal structures. This approach allows one to directly implement such patterns on any parser generated by a parser generation framework, without asking the pattern writer to learn the AST structure and node types. As a use case for that approach we describe the implementation built on top of the SmaCC (Smalltalk Compiler Compiler) GLR parser generator framework. This approach has been used in production for source code transformations on a large scale. We will express perspectives to adapt this approach to other types of parsing technologies.

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Section: Scalabilitymentioning

confidence: 95%

A Reflexive and Automated Approach to Syntactic Pattern Matching in Code Transformations

Lecerf¹,

Brant²,

Goubier³

et al. 2018

2018 IEEE International Conference on Software Maintenance and Evolution (ICSME)

View full text Add to dashboard Cite

show abstract

“…There are a number of academic tools that allow you to develop grammars such as ASF+ SDF's meta-environment [11], LAUNCHPADS [21], Synthesizer Generator [31] (has now become commercial), SmartTools [32], LISA [33], and GTB [34] (Grammar ToolBox). Meta-environment has grammar-aware editing, on-the-fly parser generation (supporting immediate grammar testing), GLR parse forest visualization, and some nice features that identify grammar issues such as useless symbols, typos, and inconsistent priorities and associativity.…”

Section: Related Workmentioning

confidence: 99%

ANTLRWorks: an ANTLR grammar development environment

Bovet¹,

Parr

2008

Softw Pract Exp

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Programmers tend to avoid using language tools, resorting to ad hoc methods, because tools can be hard to use, their parsing strategies can be difficult to understand and debug, and their generated parsers can be opaque black-boxes. In particular, there are two very common difficulties encountered by grammar developers: understanding why a grammar fragment results in a parser non-determinism and determining why a generated parser incorrectly interprets an input sentence. This paper describes ANTLRWorks, a complete development environment for ANTLR grammars that attempts to resolve these difficulties and, in general, make grammar development more accessible to the average programmer. The main components are a grammar editor with refactoring and navigation features, a grammar interpreter, and a domain-specific grammar debugger. ANTLRWorks' primary contributions are a parser non-determinism visualizer based on syntax diagrams and a time-traveling debugger that pays special attention to parser decision-making by visualizing lookahead usage and speculative parsing during backtracking. Copyright are written entirely by hand without the use of automated language tools such as parser generators, tree walker generators, and other code generators.Programmers tend to avoid using language tools, resorting to ad hoc methods, partly because of the raw and low-level interface to these tools. The threat of having to contort grammars to resolve parser non-determinisms is enough to induce many programmers to build recursive-descent parsers by hand; some readers are familiar with LALR reduce-reduce warnings from YACC [2] or LL warnings from other parser generators. Programmers commonly resort to hand-built parsers despite the fact that grammars offer a more natural, high-fidelity, robust, and maintainable means of encoding a language-related problem.The ANTLR parser generator [3] attempts to make grammars more accessible to the average programmer by accepting a larger class of grammars than LL(k) and generating recursive-descent parsers that are very similar to what a programmer would build by hand. Still, developing grammars is a non-trivial task. Just as developers use integrated development environments to dramatically improve their productivity, programmers need a sophisticated development environment for building, understanding, and debugging grammars. Unfortunately, most grammar development is done today with a simple text editor.This paper introduces ANTLRWorks, a domain-specific development environment for ANTLR version 3 grammars that we built in order to: ANTLRWORKS 1307 immediate feedback about their correctness. Developers tend to test methods, rather than waste time mentally checking the functionality of a method, because it is so quick and easy. Similarly, being able to dynamically test rules as they are written can dramatically reduce development time.Once a grammar is more-or-less complete and the generated parser has been integrated into a larger application, the grammar interpreter is less useful primarily because...

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“…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: 99%

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

Johnstone

Scott

2007

Science of Computer Programming

Self Cite

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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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Evaluating GLR parsing algorithms

Cited by 12 publications

References 10 publications

A Reflexive and Automated Approach to Syntactic Pattern Matching in Code Transformations

A Reflexive and Automated Approach to Syntactic Pattern Matching in Code Transformations

ANTLRWorks: an ANTLR grammar development environment

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

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