POSIX lexing with derivatives of regular expressions (proof pearl)

Brzozowski introduced the notion of derivatives for regular expressions. They can be used for a very simple regular expression matching algorithm. Sulzmann and Lu cleverly extended this algorithm in order to deal with POSIX matching, which is the underlying disambiguation strategy for regular expressions needed in lexers. Sulzmann and Lu have made available on-line what they call a “rigorous proof” of the correctness of their algorithm w.r.t. their specification; regrettably, it appears to us to have unfillable gaps. In the first part of this paper we give our inductive definition of what a POSIX value is and show (i) that such a value is unique (for given regular expression and string being matched) and (ii) that Sulzmann and Lu’s algorithm always generates such a value (provided that the regular expression matches the string). We also prove the correctness of an optimised version of the POSIX matching algorithm. Our definitions and proof are much simpler than those by Sulzmann and Lu and can be easily formalised in Isabelle/HOL. In the second part we analyse the correctness argument by Sulzmann and Lu and explain why the gaps in this argument cannot be filled easily.Postprin

Optimally Streaming Greedy Regular Expression Parsing

Abstract. We study the problem of streaming regular expression parsing: Given a regular expression and an input stream of symbols, how to output a serialized syntax tree representation as an output stream during input stream processing. We show that optimally streaming regular expression parsing, outputting bits of the output as early as is semantically possible for any regular expression of size m and any input string of length n, can be performed in time O(2 m log m + mn) on a unit-cost random-access machine. This is for the wide-spread greedy disambiguation strategy for choosing parse trees of grammatically ambiguous regular expressions. In particular, for a fixed regular expression, the algorithm's run-time scales linearly with the input string length. The exponential is due to the need for preprocessing the regular expression to analyze state coverage of its associated NFA, a PSPACE-hard problem, and tabulating all reachable ordered sets of NFA-states. Previous regular expression parsing algorithms operate in multiple phases, always requiring processing or storing the whole input string before outputting the first bit of output, not only for those regular expressions and input prefixes where reading to the end of the input is strictly necessary

Stream Processing using Grammars and Regular Expressions

In this dissertation we study regular expression based parsing and the use of grammatical specifications for the synthesis of fast, streaming string-processing programs. In the first part we develop two linear-time algorithms for regular expression based parsing with Perl-style greedy disambiguation. The first algorithm operates in two passes in a semi-streaming fashion, using a constant amount of working memory and an auxiliary tape storage which is written in the first pass and consumed by the second. The second algorithm is a single-pass and optimally streaming algorithm which outputs as much of the parse tree as is semantically possible based on the input prefix read so far, and resorts to buffering as many symbols as is required to resolve the next choice. Optimality is obtained by performing a PSPACE-complete pre-analysis on the regular expression. In the second part we present Kleenex, a language for expressing high-performance streaming string processing programs as regular grammars with embedded semantic actions, and its compilation to streaming string transducers with worst-case linear-time performance. Its underlying theory is based on transducer decomposition into oracle and action machines, and a finite-state specialization of the streaming parsing algorithm presented in the first part. In the second part we also develop a new linear-time streaming parsing algorithm for parsing expression grammars (PEG) which generalizes the regular grammars of Kleenex. The algorithm is based on a bottom-up tabulation algorithm reformulated using least fixed points and evaluated using an instance of the chaotic iteration scheme by Cousot and Cousot