See-Saw Generative Mechanism for Scalable Recursive Code Generation with Generative AI

Autor:	Vsevolodovna, Ruslan Idelfonso Magaña
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Software Engineering 68T05 (Primary) 68T50 68N99(Secondary) I.2.6 I.2.7 D.2.2
Druh dokumentu:	Working Paper
Popis:	The generation of complex, large-scale code projects using generative AI models presents challenges due to token limitations, dependency management, and iterative refinement requirements. This paper introduces the See-Saw generative mechanism, a novel methodology for dynamic and recursive code generation. The proposed approach alternates between main code updates and dependency generation to ensure alignment and functionality. By dynamically optimizing token usage and incorporating key elements of the main code into the generation of dependencies, the method enables efficient and scalable code generation for projects requiring hundreds of interdependent files. The mechanism ensures that all code components are synchronized and functional, enabling scalable and efficient project generation. Experimental validation demonstrates the method's capability to manage dependencies effectively while maintaining coherence and minimizing computational overhead. Comment: 18 pages, 4 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.10861 Zobrazit plný text záznamu View this record from Arxiv