Modular semantic actions

Warth, Alessandro; Dubroy, Patrick; Garnock-Jones, Tony

doi:10.1145/2989225.2989231

Cited by 5 publications

(1 citation statement)

References 9 publications

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“…Dubroy and Warth (2017) add incrementality to packrat parsing by taking the memo table from a previous parse, removing entries from it that can conflict with the edit, and starting the parser with this updated memo table. They have implemented this parsing algorithm in Ohm, a parser generator toolkit based on Parsing Expression Grammars (Warth, Dubroy, and Garnock-Jones 2016). The source code for this parser and some visualizations can be found online.…”

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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Section: Incremental Recursive Descent Parsersmentioning

confidence: 99%