Ambiguity Detection: Scaling towards Scannerless
BibTeX
@MISC{Basten_ambiguitydetection:,
author = {H. J. S. Basten and P. Klint and J. J. Vinju and Centrum Wiskunde},
title = {Ambiguity Detection: Scaling towards Scannerless},
year = {}
}
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Abstract
Abstract. Static ambiguity detection would be an important aspect of language workbenches for textual software languages. The challenge is that automatic ambiguity detection of context-free grammars is undecidable. Sophisticated approximations and optimizations do exist, but these do not scale to grammars for so-called “scannerless parsers”, as of yet. We extend previous work on ambiguity detection for context-free grammars to cover disambiguation techniques that are typical for scannerless parsing, such as longest match and reserved keywords. This paper contributes a new algorithm for ambiguity detection in character-level grammars, a prototype implementation of this algorithm and validation on several real grammars. The total run-time of ambiguity detection for character-level grammars for languages such as C and Java is dramatically reduced by several orders of magnitude, without loss of precision. The result is that ambiguity detection for realistics grammars can be done efficiently and may now become a tool in language workbenches.
Keyphrases
ambiguity detection scaling towards scannerless character-level grammar context-free grammar language workbench several order important aspect prototype implementation textual software language disambiguation technique scannerless parsing new algorithm so-called scannerless parser total run-time realistics grammar automatic ambiguity detection previous work static ambiguity detection several real grammar sophisticated approximation
