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Code generation (compiler), a mechanism to produce the executable form of computer programs, such as machine code, in some automatic manner; Automatic programming (source code generation), the act of generating source code based on an ontological model such as a template; Generating code at run time in self-modifying code and just-in-time ...
In computer science, a generator is a routine that can be used to control the iteration behaviour of a loop. All generators are also iterators. [1] A generator is very similar to a function that returns an array, in that a generator has parameters, can be called, and generates a sequence of values.
Source-code generation is the process of generating source code based on a description of the problem [9] or an ontological model such as a template and is accomplished with a programming tool such as a template processor or an integrated development environment (IDE). These tools allow the generation of source code through any of various means.
Any EMF based input (Xtext DSLs, GMF graphical models, etc.) Any textual language. actifsource: Java Active Tier User-defined Models Import from UML, Ecore. Any textual language. DMS Software Reengineering Toolkit: Several code generation DSLs (attribute grammars, tree patterns, source-to-source rewrites) Active DSLs represented as abstract ...
Procedural generation is a common technique in computer programming to automate the creation of certain data according to guidelines set by the programmer. Many games generate aspects of the environment or non-player characters procedurally during the development process in order to save time on asset creation.
In computer graphics, it is commonly used to create textures and 3D models. In video games, it is used to automatically create large amounts of content in a game. Depending on the implementation, advantages of procedural generation can include smaller file sizes, larger amounts of content, and randomness for less predictable gameplay.
However, parser generators for context-free grammars often support the ability for user-written code to introduce limited amounts of context-sensitivity. (For example, upon encountering a variable declaration, user-written code could save the name and type of the variable into an external data structure, so that these could be checked against ...
A very powerful language model called OpenAI Codex was created expressly to generate code in response to natural language commands. It is capable of understanding and producing code in a multitude of areas because it is compatible with a large number of programming languages and libraries.