Comparison of syntactic error handling in LR parsers

Degano, Pierpaolo; Priami, Corrado

doi:10.1002/spe.4380250606

Cited by 19 publications

(34 citation statements)

References 36 publications

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“…In those cases, discarding some substrings instead may be a safer alternative. The decision whether to consider particular keywords for insertion may depend on their semantic meaning and importance [Degano and Priami 1995]. To take this into account, expert feedback on a grammar is needed.…”

Section: Automatic Derivation Of Permissive Grammarsmentioning

confidence: 99%

“…Different languages also may have different points where programmers often make mistakes. Therefore a good error recovery mechanism is not only language independent, but is also flexible [Degano and Priami 1995]. That is, it allows grammar engineers to use their experience with a language to improve recovery capabilities.…”

Section: Customization Of Permissive Grammarsmentioning

confidence: 99%

“…At the point of detection of an error, the recovery mechanism is activated. A major problem for error recovery techniques is the difference between the actual location of the error and the point of detection [Degano and Priami 1995]. Consider for example the erroneous code fragment in Figure 14.…”

Section: Selecting Choice Points For Backtrackingmentioning

confidence: 99%

“…Following these criteria, an excellent recovery is one that is exactly the same as the intended program, a good recovery is one that results in a reasonable program without spurious or missed errors, and a poor recovery is a recovery that introduces spurious errors or involves excessive token deletion. The Pennello and DeRemer criteria represent the state of the art evaluation method for syntactic error recovery applied in, amongst others, [Pennello and DeRemer 1978;Pai and Kieburtz 1980;Degano and Priami 1995;Corchuelo et al 2002].…”

Section: Setupmentioning

confidence: 99%

“…The problem of handling syntax errors during parsing has been widely studied [Lévy 1971;Mauney and Fischer 1988;Pai and Kieburtz 1980;Barnard and Holt 1982;Tai 1978;Fischer et al 1980;Degano and Priami 1995;McKenzie et al 1995;Corchuelo et al 2002]. We focus on LR parsing for which there are several different error recovery techniques [Degano and Priami 1995].…”

Section: Related Workmentioning

confidence: 99%

See 4 more Smart Citations

Natural and Flexible Error Recovery for Generated Modular Language Environments

Jonge¹,

Kats²,

Visser³

et al. 2012

ACM Trans. Program. Lang. Syst.

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Integrated development environments (IDEs) increase programmer productivity, providing rapid, interactive feedback based on the syntax and semantics of a language. Unlike conventional parsing algorithms, scannerless generalized-LR parsing supports the full set of context-free grammars, which is closed under composition, and hence can parse languages composed from separate grammar modules. To apply this algorithm in an interactive environment, this paper introduces a novel error recovery mechanism. Our approach is language-independent, and relies on automatic derivation of recovery rules from grammars. By taking layout information into consideration it can efficiently suggest natural recovery suggestions.

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Section: Automatic Derivation Of Permissive Grammarsmentioning

confidence: 99%

Section: Customization Of Permissive Grammarsmentioning

confidence: 99%

Section: Selecting Choice Points For Backtrackingmentioning

confidence: 99%

Section: Setupmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Natural and Flexible Error Recovery for Generated Modular Language Environments

Jonge¹,

Kats²,

Visser³

et al. 2012

ACM Trans. Program. Lang. Syst.

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Natural and Flexible Error Recovery for Generated Parsers

Jonge

Nilsson-Nyman

Kats

et al. 2010

Software Language Engineering

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Abstract. Parser generators are an indispensable tool for rapid language development. However, they often fall short of the finesse of a hand-crafted parser, built with the language semantics in mind. One area where generated parsers have provided unsatisfactory results is that of error recovery. Good error recovery is both natural, giving recovery suggestions in line with the intention of the programmer; and flexible, allowing it to be adapted according to language insights and language changes. This paper describes a novel approach to error recovery, taking into account not only the context-free grammar, but also indentation usage. We base our approach on an extension of the SGLR parser that supports fine-grained error recovery rules and can be used to parse complex, composed languages. We take a divide-and-conquer approach to error recovery: using indentation, erroneous regions of code are identified. These regions constrain the search space for applying recovery rules, improving performance and ensuring recovery suggestions local to the error. As a last resort, erroneous regions can be discarded. Our approach also integrates bridge parsing to provide more accurate suggestions for indentation-sensitive language constructs such as scopes. We evaluate our approach by comparison with the JDT Java parser used in Eclipse.

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Bridging the chasm between MDE and the world of compilation

et al. 2012

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Modeling and transforming have always been the cornerstones of software system development, albeit often investigated by different research communities. Modeling addresses how information is represented and processed, while transformation cares about what the results of processing this information are. To address the growing complexity of software systems, Model-Driven Engineering (MDE) leverages Domain Specific Languages (DSL) to define abstract models of systems and automated methods to process them. Meanwhile, compiler technology mostly concentrates on advanced techniques and tools for program transformation. For this, it has developed complex analyses and transformations (from lexical and syntaxic to semantic analyses, down to platformspecific optimizations). These two communities appear today quite complementary and are starting to meet again in the Software Language Engineering (SLE) field. SLE addresses all the stages of a software language lifecycle, from its definition to its tooling. In this article, we show how SLE can lean on the expertise of both MDE and compiler research communities and how each community can bring its solutions to the other one. We then draw a picture of the current state of SLE, and of the challenges it has still to face.

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Comparison of syntactic error handling in LR parsers

Cited by 19 publications

References 36 publications

Natural and Flexible Error Recovery for Generated Modular Language Environments

Natural and Flexible Error Recovery for Generated Modular Language Environments

Natural and Flexible Error Recovery for Generated Parsers

Bridging the chasm between MDE and the world of compilation

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