A forward move algorithm for LR error recovery

Pennello, Thomas J.; DeRemer, Frank

doi:10.1145/512760.512786

Cited by 45 publications

(27 citation statements)

References 5 publications

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“…We use two methods to measure the quality of the recovery results. First, we do a manual inspection of the pretty-printed results, following the quality criteria of Pennello and DeRemer [1978]. 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.…”

Section: Setupmentioning

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%

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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: Setupmentioning

confidence: 99%

Section: Setupmentioning

confidence: 99%

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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show abstract

“…The quality of a repair is usually measured using the categories proposed by Pennello and DeRemer [1978]. Table III presents a summary that compares our algorithm with other authors' proposals that are examined in Section 5.…”

Section: Repairsmentioning

confidence: 99%

“…Fischer and LeBlanc's method even requires using a special LR closure algorithm in order to generate least-cost continuations and the resulting parsing tables may be slightly larger than usual. Pennello and DeRemer [1978] and Burke and Fisher [1987] presented two proposals that deserve special attention. The former is an implementation of the general idea by Graham and Rhodes [1975] in the context of LR parsers that improves the proposal by Mickunas and Modry [1978].…”

Section: Related Workmentioning

confidence: 99%

“…This motivated several researchers to work on error-repair methods that are more powerful because they attempt to recover by repairing the input string so that it becomes syntactically valid [Pennello and DeRemer 1978;Röhrich 1980;Burke and Fisher 1987;Fischer and Mauney 1992;Dain 1994]. These techniques are more difficult to implement than simple recovery because they may require modification of a part of the input already parsed or the remaining input string; some such techniques require precalculation of auxiliary tables that are used to decide which kind of repair should be used when an error is detected, which also requires modification and adaptation of existing parser generators to calculate them.…”

Section: Introductionmentioning

confidence: 99%

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Repairing syntax errors in LR parsers

Corchuelo

Pérez

Ruiz

et al. 2002

ACM Trans. Program. Lang. Syst.

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This article reports on an error-repair algorithm for LR parsers. It locally inserts, deletes or shifts symbols at the positions where errors are detected, thus modifying the right context in order to resume parsing on a valid piece of input. This method improves on others in that it does not require the user to provide additional information about the repair process, it does not require precalculation of auxiliary tables, and it can be easily integrated into existing LR parser generators. A Yacc-based implementation is presented along with some experimental results and comparisons with other well-known methods.

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Incremental parsing in language‐based editors: user needs and how to meet them

Cook

Welsh

2001

Softw Pract Exp

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Incremental parsing has long been recognized as a technique of great utility in the construction of language‐based editors, and correspondingly, the area currently enjoys a mature theory. Unfortunately, many practical considerations have been largely overlooked in previously published algorithms. Many user requirements for an editing system necessarily impact on the design of its incremental parser, but most approaches focus only on one: response time. This paper details an incremental parser based on LR parsing techniques and designed for use in a modeless syntax recognition editor. The nature of this editor places significant demands on the structure and quality of the document representation it uses, and hence, on the parser. The strategy presented here is novel in that both the parser and the representation it constructs are tolerant of the inevitable and frequent syntax errors that arise during editing. This is achieved by a method that differs from conventional error repair techniques, and that is more appropriate for use in an interactive context. Furthermore, the parser aims to minimize disturbance to this representation, not only to ensure other system components can operate incrementally, but also to avoid unfortunate consequences for certain user‐oriented services. The algorithm is augmented with a limited form of predictive tree‐building, and a technique is presented for the determination of valid symbols for menu‐based insertion. Copyright © 2001 John Wiley & Sons, Ltd.

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A forward move algorithm for LR error recovery

Abstract: David,~ryh e

Cited by 45 publications

References 5 publications

Natural and Flexible Error Recovery for Generated Modular Language Environments

Natural and Flexible Error Recovery for Generated Modular Language Environments

Repairing syntax errors in LR parsers

Incremental parsing in language‐based editors: user needs and how to meet them

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