Detecting and explaining (in)equivalence of context-free grammars
We propose a scalable framework for deciding, proving, and explaining (in)equivalence of context-free grammars. We present an implementation of the framework and evaluate it on large data sets collected within educational support systems. Even though the equivalence problem for context-free language...
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| creator | Schmellenkamp, Marko Zeume, Thomas Argo, Sven Kiefer, Sandra Siems, Cedric Stebel, Fynn |
| description | We propose a scalable framework for deciding, proving, and explaining
(in)equivalence of context-free grammars. We present an implementation of the
framework and evaluate it on large data sets collected within educational
support systems. Even though the equivalence problem for context-free languages
is undecidable in general, the framework is able to handle a large portion of
these datasets. It introduces and combines techniques from several areas, such
as an abstract grammar transformation language to identify equivalent grammars
as well as sufficiently similar inequivalent grammars, theory-based comparison
algorithms for a large class of context-free languages, and a
graph-theory-inspired grammar canonization that allows to efficiently identify
isomorphic grammars. |
| doi_str_mv | 10.48550/arxiv.2407.18220 |
| format | Article |
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(in)equivalence of context-free grammars. We present an implementation of the
framework and evaluate it on large data sets collected within educational
support systems. Even though the equivalence problem for context-free languages
is undecidable in general, the framework is able to handle a large portion of
these datasets. It introduces and combines techniques from several areas, such
as an abstract grammar transformation language to identify equivalent grammars
as well as sufficiently similar inequivalent grammars, theory-based comparison
algorithms for a large class of context-free languages, and a
graph-theory-inspired grammar canonization that allows to efficiently identify
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(in)equivalence of context-free grammars. We present an implementation of the
framework and evaluate it on large data sets collected within educational
support systems. Even though the equivalence problem for context-free languages
is undecidable in general, the framework is able to handle a large portion of
these datasets. It introduces and combines techniques from several areas, such
as an abstract grammar transformation language to identify equivalent grammars
as well as sufficiently similar inequivalent grammars, theory-based comparison
algorithms for a large class of context-free languages, and a
graph-theory-inspired grammar canonization that allows to efficiently identify
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(in)equivalence of context-free grammars. We present an implementation of the
framework and evaluate it on large data sets collected within educational
support systems. Even though the equivalence problem for context-free languages
is undecidable in general, the framework is able to handle a large portion of
these datasets. It introduces and combines techniques from several areas, such
as an abstract grammar transformation language to identify equivalent grammars
as well as sufficiently similar inequivalent grammars, theory-based comparison
algorithms for a large class of context-free languages, and a
graph-theory-inspired grammar canonization that allows to efficiently identify
isomorphic grammars.</abstract><doi>10.48550/arxiv.2407.18220</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Computers and Society Computer Science - Formal Languages and Automata Theory Computer Science - Programming Languages |
| title | Detecting and explaining (in)equivalence of context-free grammars |
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