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Regular Expression Flavor Comparison – Detailed comparison of the most popular regular expression flavors; Regexp Syntax Summary; Online Regular Expression Testing – with support for Java, JavaScript, .Net, PHP, Python and Ruby; Implementing Regular Expressions – series of articles by Russ Cox, author of RE2; Regular Expression Engines
Regular languages are a category of languages (sometimes termed Chomsky Type 3) which can be matched by a state machine (more specifically, by a deterministic finite automaton or a nondeterministic finite automaton) constructed from a regular expression. In particular, a regular language can match constructs like "A follows B", "Either A or B ...
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Ragel's input is a regular expression only in the sense that it describes a regular language; it is usually not written in a concise regular expression, but written out into multiple parts like in Extended Backus–Naur form. For example, instead of supporting POSIX character classes in regex syntax, Ragel implements them as built-in production ...
Regular expressions are used in search engines, in search and replace dialogs of word processors and text editors, in text processing utilities such as sed and AWK, and in lexical analysis. Regular expressions are supported in many programming languages. Library implementations are often called an "engine", [4] [5] and many of these are ...
The following may seem trivial for Unix-experienced people, but a collection of ready-to-use regular expressions can help people who don't know anything or much about regular expressions. For these people: You can use regular expressions in many editors to perform complex editing tasks. This will give the headings more (or fewer) equal signs (=).
Besides the built-in RE/flex POSIX regex pattern matcher, RE/flex also supports PCRE2, Boost.Regex and std::regex pattern matching libraries. PCRE2 and Boost.Regex offer a richer regular expression pattern syntax with Perl pattern matching semantics, but are slower due to their intrinsic NFA-based matching algorithm.
The system uses a DFA for lexical analysis and the LALR algorithm for parsing. Both of these algorithms are state machines that use tables to determine actions. GOLD is designed around the principle of logically separating the process of generating the LALR and DFA parse tables from the actual implementation of the parsing algorithms themselves.