More About Converting BNF to PEG

Redziejowski, Roman R.

doi:10.3233/fi-2014-1075

Cited by 4 publications

(4 citation statements)

References 4 publications

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“…In order to define a safe input position to resume parsing, we will use the classical F IRST and F OLLOW sets. A detailed discussion about F IRST and F OLLOW sets in the context of PEGs can be found in other papers [8,9,10].…”

Section: Error Recovery In Pegs With Labeled Failuresmentioning

confidence: 99%

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Automatic syntax error reporting and recovery in parsing expression grammars

Medeiros

Alvez

Mascarenhas

2020

Science of Computer Programming

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Error recovery is an essential feature for a parser that should be plugged in Integrated Development Environments (IDEs), which must build Abstract Syntax Trees (ASTs) even for syntactically invalid programs in order to offer features such as automated refactoring and code completion.Parsing Expressions Grammars (PEGs) are a formalism that naturally describes recursive top-down parsers using a restricted form of backtracking. Labeled failures are a conservative extension of PEGs that adds an error reporting mechanism for PEG parsers, and these labels can also be associated with recovery expressions to provide an error recovery mechanism. These expressions can use the full expressivity of PEGs to recover from syntactic errors.Manually annotating a large grammar with labels and recovery expressions can be difficult. In this work, we present algorithms that automatically annotates a PEG with labels, and build their corresponding recovery expressions.We evaluate these algorithms by using them to generate error recovering parsers for four programming languages: Titan, Pascal, C and Java. The results show that with a small amount of manual intervention our approach can be used to produce error recovering parsers for PEGs where most choices have alternatives with disjoint F IRST sets.

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Section: Error Recovery In Pegs With Labeled Failuresmentioning

confidence: 99%

“…http://bit.ly/c89-manual9 Coincidentally, both Titan and C grammars have the same number of syntactical rules, and their manually annotated grammars throw the same amount of labels.10 http://bit.ly/c89-standard…”

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confidence: 99%

Automatic syntax error reporting and recovery in parsing expression grammars

Medeiros

Alvez

Mascarenhas

2020

Science of Computer Programming

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“…We can improve the error recovery quality of a PEG parser by using the FIRST and FOLLOW sets of parsing expressions when throwing labels or recovering from an error. A detailed discussion about FIRST and FOLLOW sets in the context of PEGs can be found in other papers [12,20,21].…”

Section: Error Recovery Strategies For Pegsmentioning

confidence: 99%

Syntax error recovery in parsing expression grammars

Medeiros

Mascarenhas²

2018

Proceedings of the 33rd Annual ACM Symposium on Applied Computing

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Parsing Expression Grammars (PEGs) are a formalism used to describe top-down parsers with backtracking. As PEGs do not provide a good error recovery mechanism, PEG-based parsers usually do not recover from syntax errors in the input, or recover from syntax errors using ad-hoc, implementation-specific features. e lack of proper error recovery makes PEG parsers unsuitable for using with Integrated Development Environments (IDEs), which need to build syntactic trees even for incomplete, syntactically invalid programs.We propose a conservative extension, based on PEGs with labeled failures, that adds a syntax error recovery mechanism for PEGs. is extension associates recovery expressions to labels, where a label now not only reports a syntax error but also uses this recovery expression to reach a synchronization point in the input and resume parsing. We give an operational semantics of PEGs with this recovery mechanism, and use an implementation based on such semantics to build a robust parser for the Lua language. We evaluate the effectiveness of this parser, alone and in comparison with a Lua parser with automatic error recovery generated by ANTLR, a popular parser generator.

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Section: Error Recovery Strategies For Pegsmentioning

confidence: 99%