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  • 1.
    Drewes, Frank
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Hoffmann, Berthold
    University of Bremen.
    Minas, Mark
    Universität der Bundeswehr München.
    Formalization and Correctness of Predictive Shift-Reduce Parsers for Graph Grammars Based on Hyperedge Replacement2019In: The Journal of logical and algebraic methods in programming, ISSN 2352-2208, E-ISSN 2352-2216, Vol. 104, p. 303-341Article in journal (Refereed)
    Abstract [en]

    Hyperedge replacement (HR) grammars can generate NP-complete graph languages, which makes parsing hard even for fixed HR languages. Therefore, we study predictive shift-reduce (PSR) parsing that yields efficient parsers for a subclass of HR grammars, by generalizing the concepts of SLR(1) string parsing to graphs. We formalize the construction of PSR parsers and show that it is correct. PSR parsers run in linear space and time, and are more efficient than the predictive top-down (PTD) parsers recently developed by the authors.

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