Bridging gaps in code generation with large language models

Large Language Models (LLMs) are transforming natural language processing and extending their impact to code generation. This thesis evaluates both academic and industrial LLMs, focusing on their ability to generate pragmatic, functional code for non-standalone functions—a critical aspect of real-wo...

Full description

Autores:
Osorio Cálad, Juan José
Tipo de recurso:
Trabajo de grado de pregrado
Fecha de publicación:
2025
Institución:
Universidad de los Andes
Repositorio:
Séneca: repositorio Uniandes
Idioma:
eng
OAI Identifier:
oai:repositorio.uniandes.edu.co:1992/75375
Acceso en línea:
https://hdl.handle.net/1992/75375
Palabra clave:
Bridging the Industry–Academia Gap
Large Language Models
Model Evaluation
Code Generation
Ingeniería
Rights
openAccess
License
Attribution 4.0 International