V-Star：从程序输入中学习可见下推语法
V-Star: Learning Visibly Pushdown Grammars from Program Inputs
AI总结：
本文提出 V-Star，通过主动学习可见下推自动机并改造 L-Star，从程序样本中推断嵌套结构，实现对 JSON、XML 等实用语法的精确高效学习。
摘要：
准确描述程序输入仍是编程语言领域的一项关键挑战。主动学习作为一个成熟的领域，可实现正则语言的精确学习。我们提供了一款创新的语法推断工具 V-Star，它基于可见下推自动机的主动学习。V-Star 从样本输入中推断程序输入语言的嵌套结构，并采用一种基于嵌套模式的新型推断机制。该机制识别 token 边界，并将 XML 文档等语言转换为 VPL。随后，我们将 Angluin 的 L-Star 这一精确学习算法改造用于 VPA 学习，从而提高了工具的精度。我们的评估表明，V-Star 能够有效且高效地学习多种实用语法，包括 S-Expressions、JSON 和 XML，并且优于其他最先进的工具。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
