Ambiguity Detection: Scaling to Scannerless

  • Hendrikus J. S. Basten
  • Paul Klint
  • Jurgen J. Vinju
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6940)


Static ambiguity detection would be an important aspect of language workbenches for textual software languages. However, the challenge is that automatic ambiguity detection in context-free grammars is undecidable in general. 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 significantly reduced, without loss of precision. The result is that efficient ambiguity detection in realistic grammars is possible and may therefore become a tool in language workbenches.


Production Rule Character Class Parse Tree Baseline Algorithm Ambiguous Sentence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hendrikus J. S. Basten
    • 1
  • Paul Klint
    • 1
  • Jurgen J. Vinju
    • 1
  1. 1.Centrum Wiskunde & InformaticaAmsterdamThe Netherlands

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