  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.
