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Several code generation DSLs (attribute grammars, tree patterns, source-to-source rewrites) Active. DSLs represented as abstract syntax trees. DSL instance. Well-formed output language code fragments. Any programming language (proven for C, C++, Java, C#, PHP, COBOL) DRAKON.
full semantic analysis of source code, including parameter types, conditional compilation directives, macro expansions Javadoc: JSDoc: Yes JsDoc Toolkit: Yes mkd: Customisable for all type of comments 'as-is' in comments all general documentation; references, manual, organigrams, ... Including the binary codes included in the comments. all ...
HTTP. HTTP header fields are a list of strings sent and received by both the client program and server on every HTTP request and response. These headers are usually invisible to the end-user and are only processed or logged by the server and client applications. They define how information sent/received through the connection are encoded (as in ...
Bootstrapping (compilers) In computer science, bootstrapping is the technique for producing a self-compiling compiler – that is, a compiler (or assembler) written in the source programming language that it intends to compile. An initial core version of the compiler (the bootstrap compiler) is generated in a different language (which could be ...
Sass, Less Framework, Bootstrap (v3) Less ( Leaner Style Sheets; sometimes stylized as LESS) is a dynamic preprocessor style sheet language that can be compiled into Cascading Style Sheets (CSS) and run on the client side or server side. [2] Designed by Alexis Sellier, Less is influenced by Sass and has influenced the newer "SCSS" syntax of ...
Code generation (compiler) In computing, code generation is part of the process chain of a compiler and converts intermediate representation of source code into a form (e.g., machine code) that can be readily executed by the target system. Sophisticated compilers typically perform multiple passes over various intermediate forms.
Artificial intelligence and machine learning. Bootstrapping is a technique used to iteratively improve a classifier 's performance. Typically, multiple classifiers will be trained on different sets of the input data, and on prediction tasks the output of the different classifiers will be combined.
Bootstrapping is any test or metric that uses random sampling with replacement (e.g. mimicking the sampling process), and falls under the broader class of resampling methods. Bootstrapping assigns measures of accuracy ( bias, variance, confidence intervals, prediction error, etc.) to sample estimates.