A simple and efficient incremental LL(1) parsing

Li, Warren X.

doi:10.1007/3-540-60609-2_24

Cited by 7 publications

(5 citation statements)

References 4 publications

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“…Research on incremental parsing has focused on LR parsing [Ghezzi and Mandrioli 1980;Wagner and Graham 1998;Jalili and Gallier 1982;Larcheveque 1995;Petrone 1995] and LL (recursive descent parsing) [Murching et al 1990;Li 1995;Linden 1993], since programming languages are typically described by LR(0), LR(1), LL(1), LALR(1), and LL(1) grammars. All techniques start by parsing the input text and producing a parse tree, which is typically annotated with auxiliary information.…”

Section: Related Workmentioning

confidence: 99%

Incremental validation of XML documents

Balmin

Papakonstantinou

Vianu

2004

ACM Trans. Database Syst.

110

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We investigate the incremental validation of XML documents with respect to DTDs, specialized DTDs, and XML Schemas, under updates consisting of element tag renamings, insertions, and deletions. DTDs are modeled as extended context-free grammars. "Specialized DTDs" allow the decoupling of element types from element tags. XML Schemas are abstracted as specialized DTDs with limitations on the type assignment. For DTDs and XML Schemas, we exhibit an O(m log n) incremental validation algorithm using an auxiliary structure of size O(n), where n is the size of the document and m the number of updates. The algorithm does not handle the incremental validation of XML Schema wrt renaming of internal nodes, which is handled by the specialized DTDs incremental validation algorithm. For specialized DTDs, we provide an O(m log 2 n) incremental algorithm, again using an auxiliary structure of size O(n). This is a significant improvement over brute-force re-validation from scratch.We exhibit a restricted class of DTDs called local that arise commonly in practice and for which incremental validation can be done in practically constant time by maintaining only a list of counters. We present implementations of both general incremental validation and local validation on an XML database built on top of a relational database.Our experimentation includes a study of the applicability of local validation in practice, results on the calibration of parameters of the auxiliary data structure, and results on the performance comparison between the general incremental validation technique, the local validation technique, and brute-force validation from scratch.

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Section: Related Workmentioning

confidence: 99%

Incremental validation of XML documents

Balmin

Papakonstantinou

Vianu

2004

ACM Trans. Database Syst.

110

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“…Incremental LL( 1) Yang (1993) and Li (1995), among others, have introduced algorithms for incremental LL( 1) parsing. Both incremental LL(1) parsing algorithms "cut" the previous parse tree at the location of an edit: say that the input xyz is edited to xȳz, parse tree X is equal to the previous parse tree with holes at the locations where the parse nodes for substrings y and z would be, and Z is a list of subtrees that represent parts of z.…”

Section: Incremental Recursive Descent Parsersmentioning

confidence: 99%

“…The algorithm of Yang (1993) decides whether it can reuse a subtree in Z by using its first terminal node to index the parse table, whereas Li (1995) improved this by allowing the parse table to contain non-terminals so that the parser can use the symbol in the root of a subtree to determine the next parse action. The latter approach is equal to how the ILR parser by Wagner and Graham (1997b) tests if it can reuse a parse node.…”

Section: Incremental Recursive Descent Parsersmentioning

confidence: 99%

Incremental scannerless generalized LR parsing

Sijm

2019

Proceedings Companion of the 2019 ACM SIGPLAN International Conference on Systems, Programming, Languages, and Applications: So

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The Scannerless Generalized LR (SGLR) parsing algorithm supports the development of composed languages seamlessly but does not support incremental parsing. The Incremental Generalized LR (IGLR) parsing algorithm, on the other hand, does not support the seamless composition of languages. This thesis presents the Incremental Scannerless Generalized LR (ISGLR) parsing algorithm and investigates the effects of combining the SGLR and IGLR parsing algorithms. While the algorithmic differences are orthogonal, the fact that scannerless parsing relies on non-deterministic parsing for disambiguation has a negative impact on incrementality. Nonetheless, we show that the ISGLR parsing algorithm performs better than the batch SGLR parsing algorithm in typical scenarios. On average, the ISGLR parser can reuse 99% of a previous parse result. When parsing from scratch, the ISGLR parser has a 24% run time overhead compared to the SGLR parser, but when parsing incrementally for changes that are smaller than 1% of the input size on average, it has a 9× speedup.

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“…There have been suggested many methods for incremental analysis. Algorithms of incremental compilation of context-free language class LL (Yang, 1993;Li, 1995;Li, 1996;Shilling, 1992;Murching, Prasad and Srikant, 1990;Ferro and Dion, 1994) and LR (Yang, 1994;Agrawal and Detrich, 1983;Tomita, 1987;Horspool, 1990;Wagner and Graham, 1997;Wagner and Graham, 1998;Wagner, 1998) have been published. Melichar and Vagner (2008) published an elementary text about how to create an incremental parser of LL(1) languages as a part of educational materials in English, based on previous publications of Li (1995), but suitably accompanied by examples and illustrations.…”

Section: Overview Of Literature and The Present Statementioning

confidence: 99%