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In computer science, the Krauss wildcard-matching algorithm is a pattern matching algorithm. Based on the wildcard syntax in common use, e.g. in the Microsoft Windows command-line interface, the algorithm provides a non-recursive mechanism for matching patterns in software applications, based on syntax simpler than that typically offered by regular expressions.
It is a simplification of the Boyer–Moore string-search algorithm which is related to the Knuth–Morris–Pratt algorithm. The algorithm trades space for time in order to obtain an average-case complexity of O(n) on random text, although it has O(nm) in the worst case, where the length of the pattern is m and the length of the search string ...
C, C++, C# Java, Kotlin JavaScript, TypeScript .NET Python Go, Ruby A code searching tool with an emphasis on finding software bugs. Search patterns are written in a query language which can search the AST and graphs (CFG, DFG, etc.) of supported languages. A plugin is available for Visual Studio. ConQAT (retired) 2015-02-01 Yes; ASL 2: Ada C#, C++
When investigating the possibility of removing friend name injection from the C++ programming language, Barton and Nackman's idiom was found to be the only reasonable use of that language rule. Eventually, the rules for argument-dependent lookup were adjusted [ 2 ] to replace friend name injection by a less drastic mechanism, described above ...
Various implementations exist in different programming languages. In C++ it is part of the Standard Library since C++17 and Boost provides the generic Boyer–Moore search implementation under the Algorithm library. In Go (programming language) there is an implementation in search.go.
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.
In computer science, the two-way string-matching algorithm is a string-searching algorithm, discovered by Maxime Crochemore and Dominique Perrin in 1991. [1] It takes a pattern of size m, called a “needle”, preprocesses it in linear time O(m), producing information that can then be used to search for the needle in any “haystack” string, taking only linear time O(n) with n being the ...
Uses of pattern matching include outputting the locations (if any) of a pattern within a token sequence, to output some component of the matched pattern, and to substitute the matching pattern with some other token sequence (i.e., search and replace). Sequence patterns (e.g., a text string) are often described using regular expressions and ...