Relevant bibliographies by topics / LALR parser generator

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters

Academic literature on the topic 'LALR parser generator'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "LALR parser generator"

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Shin, Heung-Chul, and Kwang-Moo Choe. "An improved LALR(k) parser generation for regular right part grammars." Information Processing Letters 47, no. 3 (1993): 123–29. http://dx.doi.org/10.1016/0020-0190(93)90235-2.

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2

Nakata, Ikuo, and Masataka Sassa. "Generation of efficient LALR parsers for regular right part grammars." Acta Informatica 23, no. 2 (1986): 149–62. http://dx.doi.org/10.1007/bf00289495.

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Morimoto, Shin-ichi, and Masataka Sassa. "Yet another generation of LALR parsers for regular right part grammars." Acta Informatica 37, no. 9 (2001): 671–97. http://dx.doi.org/10.1007/pl00013318.

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Costagliola, Gennaro, Mattia De Rosa, Vittorio Fuccella, and Mark Minas. "Visual exploration of visual parser execution." Multimedia Tools and Applications, March 18, 2021. http://dx.doi.org/10.1007/s11042-021-10624-6.

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AbstractIn 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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Dissertations / Theses on the topic "LALR parser generator"

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Preußer, Thomas. "The wisent Parser Generator." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-98686.

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Objective: This document is not an introduction to parser generators. Readers should rather have a good understanding of them, especially of LALR parser generators. This document merely describes the main aspects of the implementation of a LALR parser generator named wisent. It is divided into a concise description of the programming interface and an overview over the data structures and specifics of the implementation of the wisent parser generator.
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Preußer, Thomas. "The wisent Parser Generator." Technische Universität Dresden, 2004. https://tud.qucosa.de/id/qucosa%3A26195.

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Objective: This document is not an introduction to parser generators. Readers should rather have a good understanding of them, especially of LALR parser generators. This document merely describes the main aspects of the implementation of a LALR parser generator named wisent. It is divided into a concise description of the programming interface and an overview over the data structures and specifics of the implementation of the wisent parser generator.
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Lopez-Rojas, Edgar Alonso. "OMCCp : A MetaModelica Based Parser Generator Applied to Modelica." Thesis, Linköpings universitet, PELAB - Laboratoriet för programmeringsomgivningar, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-68863.

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The OpenModelica Compiler-Compiler parser generator (OMCCp) is an LALR(1) parser generator implemented in the MetaModelica language with parsing tables generated by the tools Flex and GNU Bison. The code generated for the parser is in MetaModelica 2.0 language which is the OpenModelica compiler implementation language and is an extension of the Modelica 3.2 language. OMCCp uses as input an LALR(1) grammar that specifies the Modelica language. The generated Parser can be used inside the OpenModelica Compiler (OMC) as a replacement for the current parser generated by the tool ANTLR from an LL(k) Modelica grammar. This report explains the design and implementation of this novel Lexer and Parser Generator called OMCCp. Modelica and its extension MetaModelica are both languages used in the OpenModelica environment. Modelica is an Object-Oriented Equation-Based language for Modeling and Simulation.
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Hynek, Jiří. "Editor jazyka CodAL v prostředí Eclipse." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-412889.

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The Master thesis is focused on creation of an editor of CodAL language for the development toolkit of the project Lissom which is based on Eclipse framework. The goal of this thesis is to analyze the problem of editor creation and the features in existing editors which add some value to their usability. The outline of parser creation and subsequent code analysis of the source codes written into the editor is described in the theoretical part. It also explains the syntax and semantic aspects of the CodAL language. In the practical part the new CodAL language editor is designed and developed. The new CodAL language editor integrated into the development toolkit of the project Lissom is the final outcome of this thesis.
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Book chapters on the topic "LALR parser generator"

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Horspool, R. Nigel. "ILALR: An incremental generator of LALR(1) parsers." In Compiler Compilers and High Speed Compilation. Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/3-540-51364-7_10.

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Larchevêque, J. M. "Using an LALR compiler compiler to generate incremental parsers." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/3-540-53669-8_80.

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