Simplified Parsing Expression Derivatives

Moss, Aaron

doi:10.1007/978-3-030-40608-0_30

Cited by 6 publications

(7 citation statements)

References 16 publications

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“…This should provide more tools for PEG's user to verify whether a grammar correctly models a given language of interest. We still need to refine this work and to further investigate how it compares to other works that explored the use of derivatives in PEGs [9,21] and in CFGs [19].…”

Section: Discussionmentioning

confidence: 99%

“…One useful technique to predict the behavior of a grammar is to automatically generate strings from it and check whether the results match the intuitive behavior. There are recently works implementing derivative parsers [3] for PEGs to accomplish this task [9,21]. One of the main difficulties is precisely the backtrack mechanism in PEGs.…”

Section: B Symbolic Executions and Derivativesmentioning

confidence: 99%

“…One of the main difficulties is precisely the backtrack mechanism in PEGs. Hence, the algorithms need to keep track of the different branches [21] and compute possible over approximations [9] of different notions as testing whether an expression recognizes the empty string (which is undecidable in general [8]). This section shows that our formal specification can be adapted to implement a derivative tool.…”

Section: B Symbolic Executions and Derivativesmentioning

confidence: 99%

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A semantic framework for PEGs

Medeiros

Olarte

2020

Proceedings of the 13th ACM SIGPLAN International Conference on Software Language Engineering

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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 | ′ is interpreted as the union of the languages recognized by and ′ , the PEGs' prioritized choice / ′ discards ′ if 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.

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Section: Discussionmentioning

confidence: 99%

Section: B Symbolic Executions and Derivativesmentioning

confidence: 99%

Section: B Symbolic Executions and Derivativesmentioning

confidence: 99%

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A semantic framework for PEGs

Medeiros

Olarte

2020

Proceedings of the 13th ACM SIGPLAN International Conference on Software Language Engineering

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“…al. [41] presented another algorithm that can parse the PEG's algorithm with worst-case space and rum time complexity of linear time. Lurent et.…”

Section: Literature Reviewmentioning

confidence: 99%

LL (1) Parser versus GNF inducted LL (1) Parser on Arithmetic Expressions Grammar: A Comparative Study

Ali

Naveed

Naseem

et al. 2020

QUESTRJ

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The prime objective of the proposed study is to determine the induction of Greibach Normal Form (GNF) in Arithmetic Expression Grammars to improve the processing speed of conventional LL(1) parser. Conventional arithmetic expression grammar and its equivalent LL(1) is used in the study which is converted. A transformation method is defined which converts the selected grammar into a Greibach normal form that is further converted into a GNF based parser through a method proposed in the study. These two parsers are analyzed by considering 399 cases of arithmetic expressions. During statistical analysis, the results are initially examined with the Kolmogorov-Smirnov and Shapiro-Wilk test. The statistical significance of the proposed method is evaluated with the Mann-Whitney U test. The study described that GNF based LL(1) parser for arithmetic take fewer steps than conventional LL(1) grammar. The ranks and asymptotic significance depict that the GNF based LL(1) method is significant than the conventional LL(1) approach. The study adds to the knowledge of parsers itself, parser expression grammars (PEG’s), LL(1) grammars, Greibach Normal Form (GNF) induced grammar structure, and the induction of Arithmetic PEG’s LL(1) to GNF based grammar.

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“…Although there are at least three other Python packages for generating PEG parsers, namely Arpeggio (http://www.igordejanovic.net/Arpeggio), Grako (https://bitbucket.org/ neogeny/grako), and pyPEG (https://fdik.org/pyPEG), our implementation (https://github.com/ wieczorekw/wieczorekw.github.io/tree/master/PEG) is worth noting for its simple usage (integration with Python syntax via native operators) and because it is dozens of times faster in processing long strings, as will be shown in detail in Section 3.3. In addition to Python libraries, to enrich the research, a library named EGG (https://github.com/bruceiv/egg/tree/deriv) written in C++ was used for comparison, in which an expression has to be compiled into machine code before it is used [14].…”

Section: Introductionmentioning

confidence: 99%

Parsing Expression Grammars and Their Induction Algorithm

2020

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Grammatical inference (GI), i.e., the task of finding a rule that lies behind given words, can be used in the analyses of amyloidogenic sequence fragments, which are essential in studies of neurodegenerative diseases. In this paper, we developed a new method that generates non-circular parsing expression grammars (PEGs) and compares it with other GI algorithms on the sequences from a real dataset. The main contribution of this paper is a genetic programming-based algorithm for the induction of parsing expression grammars from a finite sample. The induction method has been tested on a real bioinformatics dataset and its classification performance has been compared to the achievements of existing grammatical inference methods. The evaluation of the generated PEG on an amyloidogenic dataset revealed its accuracy when predicting amyloid segments. We show that the new grammatical inference algorithm achieves the best ACC (Accuracy), AUC (Area under ROC curve), and MCC (Mathew’s correlation coefficient) scores in comparison to five other automata or grammar learning methods.

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Simplified Parsing Expression Derivatives

Cited by 6 publications

References 16 publications

A semantic framework for PEGs

A semantic framework for PEGs

LL (1) Parser versus GNF inducted LL (1) Parser on Arithmetic Expressions Grammar: A Comparative Study

Parsing Expression Grammars and Their Induction Algorithm

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