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In computing, code generation denotes software techniques or systems that generate program code which may then be used independently of the generator system in a runtime environment. Specific articles: Code generation (compiler), a mechanism to produce the executable form of computer programs, such as machine code, in some automatic manner
GYP (Generate Your Projects) – Build automation tool created by Google; superseded by GN which generates files for ninja and other tools; imake – build automation system written for the X Window System; OpenMake Software Meister; Meson – Build automation tool; integrated with GNOME Builder [2]
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) gSOAP: C / C++ WSDL specifications
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.
GitHub Copilot was initially powered by the OpenAI Codex, [13] which is a modified, production version of the Generative Pre-trained Transformer 3 (GPT-3), a language model using deep-learning to produce human-like text. [14]
When code generation occurs at runtime, as in just-in-time compilation (JIT), it is important that the entire process be efficient with respect to space and time. For example, when regular expressions are interpreted and used to generate code at runtime, a non-deterministic finite-state machine is often generated instead of a deterministic one, because usually the former can be created more ...
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With a focus on the meaning of code, Codota's AI-based autocompletion employed a semantic approach to automatically generate code. [14] [15] [6] Codota, the predecessor of Tabnine, secured $2 million in seed investment in June 2017. Following this, in June 2018, the company introduced the first AI-based code completion for Java IDE. [16] [10] [13]