Explorando el rol del prompt engineering en la generación de código: Una revisión sistemática
Abstract
En este repositorio encontrará una lista de documentos que ofrecen una visión general del proceso llevado a cabo en la Revisión Sistemática de la Literatura sobre el uso del Prompt Engineering en el desarrollo de software.
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Tabla. Compendio de estudios primarios resultado de la búsqueda. Id Artículo N.C Año Ref. E1 An Approach for Rapid Source Code Development Based on ChatGPT and Prompt engineering 19 2024 [21] E2 Self-collaboration code generation via chatgpt 301 2024 [22] E3 Generating Java code pairing with ChatGPT 3 2024 [1] E4 An Empirical Study of the Non-determinism of ChatGPT in Code Generation 64 2025 [23] E5 From Misuse to Mastery: Enhancing Code Generation with Knowledge-Driven AI Chaining 32 2023 [8] E6 Prompt Problems: A New Programming Exercise for the Generative AI Era 141 2024 [24] E7 Developing time series forecasting models with generative large language models 5 2024 [7] E8 Enhancing robustness of AI offensive code generators via data augmentation 6 2025 [25] E9 ANPL: towards natural programming with interactive decomposition 11 2024 [3] E10 Knowledge-Aware Code Generation with Large Language Models 16 2024 [26] E11 RGD: Multi-LLM Based Agent Debugger via Refinement and Generation Guidance 2 2024 [2] E12 A study on prompt design, advantages and limitations of ChatGPT for deep learning program repair 95 2025 [10] E13 CodeDoctor: multi-category code review comment generation 1 2025 [27] E14 LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Ahead 27 2025 [14] E15 “It’s Weird That it Knows What I Want”: Usability and Interactions with Copilot for Novice Programmers 228 2023 [6] Acrónimos usados: Id = Identificador del Estudio, NC = Número de Citaciones, Ref. = Referencias.