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Computer Science > Programming Languages

arXiv:2510.18479 (cs)
[Submitted on 21 Oct 2025 ]

Title: ZipLex: Verified Invertible Lexing with Memoized Derivatives and Zippers

Title: ZipLex:带记忆导数和Zippers的可逆词法分析验证

Authors:Samuel Chassot, Viktor Kunčak
Abstract: We present ZipLex, a verified framework for invertible lexical analysis. Unlike past verified lexers that focus only on satisfying the semantics of regular expressions and the maximal munch property, ZipLex also guarantees that lexing and printing are mutual inverses. Our design relies on two sets of ideas: (1) a new abstraction of token sequences that captures the separability of tokens in a sequence while supporting their efficient manipulation, and (2) a combination of verified data structures and optimizations, including Huet's zippers and memoized derivatives, to achieve practical performance. We implemented ZipLex in Scala and verified its correctness, including invertibility, using the Stainless verifier. Our evaluation demonstrates that ZipLex supports realistic applications such as JSON processing and lexers of programming languages. In comparison to other verified lexers (which do not enforce invertibility), ZipLex is 4x slower than Coqlex and two orders of magnitude faster than Verbatim++, showing that verified invertibility can be achieved without prohibitive cost.
Abstract: 我们提出ZipLex,一个可验证的可逆词法分析框架。 与以往只关注满足正则表达式语义和最大匹配属性的可验证词法分析器不同,ZipLex还保证词法分析和打印是互为逆运算的。 我们的设计依赖于两个方面的想法:(1) 一种新的标记序列抽象,它捕捉了序列中标记的可分离性,同时支持它们的高效操作,以及(2) 可验证数据结构和优化的结合,包括Huet的zippers和记忆导数,以实现实际性能。 我们在Scala中实现了ZipLex,并使用Stainless验证器验证了其正确性,包括可逆性。 我们的评估表明, ZipLex支持现实应用,如JSON处理和编程语言的词法分析器。 与其他不强制可逆性的可验证词法分析器相比,ZipLex比Coqlex慢4倍,但比Verbatim++快两个数量级,这表明可验证的可逆性可以在没有高昂成本的情况下实现。
Subjects: Programming Languages (cs.PL) ; Formal Languages and Automata Theory (cs.FL)
Cite as: arXiv:2510.18479 [cs.PL]
  (or arXiv:2510.18479v1 [cs.PL] for this version)
  https://doi.org/10.48550/arXiv.2510.18479
arXiv-issued DOI via DataCite

Submission history

From: Samuel Chassot [view email]
[v1] Tue, 21 Oct 2025 09:58:08 UTC (241 KB)
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