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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 ...
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
Automatic parallelization by compilers or tools is very difficult due to the following reasons: [6] dependence analysis is hard for code that uses indirect addressing, pointers, recursion, or indirect function calls because it is difficult to detect such dependencies at compile time; loops have an unknown number of iterations;
Well-formed output language code fragments Any programming language (proven for C, C++, Java, C#, PHP, COBOL) gSOAP: C / C++ WSDL specifications C / C++ code that can be used to communicate with WebServices. XML with the definitions obtained. Microsoft Visual Studio LightSwitch: C# / VB.NET Active Tier Database schema
A macro processor, such as the C preprocessor, which replaces patterns in source code according to relatively simple rules, is a simple form of source-code generator. Source-to-source code generation tools also exist. [11] [12] Large language models such as ChatGPT are capable of generating a program's source code from a description of the ...
Generative artificial intelligence (generative AI, GenAI, [1] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data.
This algorithm, also known as the "recursive backtracker" algorithm, is a randomized version of the depth-first search algorithm. Frequently implemented with a stack , this approach is one of the simplest ways to generate a maze using a computer.
Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination of automation and ML. [1] AutoML potentially includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment.