Figuring out grammars from examples

There's been a surprising amount of work on taking language strings and
feeding them to a box that infers a grammar for them. One of the harder
bits is figuring out nesting constructs, which in the past has often
required hints.


In this paper, a Visibly Pushdown Grammar is large but more tractable
subset of context free grammars.


V-Star: Learning Visibly Pushdown Grammars from Program Inputs
Xiaodong Jia, Gang Tan


Accurate description of program inputs remains a critical challenge in the
field of programming languages. Active learning, as a well-established
field, achieves exact learning for regular languages. We offer an
innovative grammar inference tool, V-Star, based on the active learning of
visibly pushdown automata. V-Star deduces nesting structures of program
input languages from sample inputs, employing a novel inference mechanism
based on nested patterns. This mechanism identifies token boundaries and
converts languages such as XML documents into VPLs. We then adapted
Angluin's L-Star, an exact learning algorithm, for VPA learning, which
improves the precision of our tool. Our evaluation demonstrates that
V-Star effectively and efficiently learns a variety of practical grammars,
including S-Expressions, JSON, and XML, and outperforms other
state-of-the-art tools.


https://arxiv.org/abs/2404.04201v1


Regards,
John Levine, johnl@taugh.com, Taughannock Networks, Trumansburg NY
Please consider the environment before reading this e-mail. https://jl.ly

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