| dc.contributor | Jordán Cordones, Freddy Maximiliano | |
| dc.creator | Basantes Suñiga, Ronny Alejandro | |
| dc.date | 2023-05-24T19:14:13Z | |
| dc.date | 2023-05-24T19:14:13Z | |
| dc.date | 2023 | |
| dc.date.accessioned | 2023-08-08T17:05:58Z | |
| dc.date.available | 2023-08-08T17:05:58Z | |
| dc.identifier | http://dspace.utb.edu.ec/handle/49000/13900 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/8043436 | |
| dc.description | This comparative study analyzes two artificial intelligence (AI) tools, Code GPT and GitHub Copilot, as programming assistants in software development. A descriptive methodology with a qualitative approach and the interview technique was used to compare the tools, analyzing their features, functionalities, efficiency and identifying their limitations. As a result, it is concluded that both Code GPT and GitHub Copilot have a very high level of training that allows them to generate high quality and accurate code. Both tools represent a breakthrough in the field of artificial intelligence for automated code generation. However, one of the limitations is sometimes the suggestion of obsolete code or code that does not comply with good programming practices because they are based on the analysis of large amounts of existing code data, which may be outdated or buggy. This study provides a deeper understanding of both tools, allowing developers to take full advantage of their functions and optimize time and work during programming in software development. | |
| dc.description | This comparative study analyzes two artificial intelligence (AI) tools, Code GPT and GitHub Copilot, as programming assistants in software development. A descriptive methodology with a qualitative approach and the interview technique was used to compare the tools, analyzing their features, functionalities, efficiency and identifying their limitations. As a result, it is concluded that both Code GPT and GitHub Copilot have a very high level of training that allows them to generate high quality and accurate code. Both tools represent a breakthrough in the field of artificial intelligence for automated code generation. However, one of the limitations is sometimes the suggestion of obsolete code or code that does not comply with good programming practices because they are based on the analysis of large amounts of existing code data, which may be outdated or buggy. This study provides a deeper understanding of both tools, allowing developers to take full advantage of their functions and optimize time and work during programming in software development. | |
| dc.description | El presente estudio comparativo analiza dos herramientas de inteligencia artificial (IA), Code GPT y GitHub Copilot, como asistentes de programación en el desarrollo de software. Se utilizó una metodología descriptiva con un enfoque cualitativo y la técnica de la entrevista para comparar las herramientas, analizando sus características, funcionalidades, eficiencia e identificando sus limitaciones. Como resultado, se concluye que tanto Code GPT como GitHub Copilot tienen un nivel de entrenamiento muy alto que les permite generar código de alta calidad y precisión. Ambas herramientas representan un gran avance en el campo de la inteligencia artificial para la generación de código automatizado. Sin embargo, una de las limitaciones es, en ocasiones, la sugerencia de código obsoleto o que no cumple con las buenas prácticas de programación debido a que se basan en el análisis de grandes cantidades de datos de código existente, que puede estar desactualizado o con errores. Este estudio proporciona una comprensión más profunda de ambas herramientas, permitiendo a los desarrolladores aprovechar al máximo sus funciones y optimizar el tiempo y trabajo durante la programación en el desarrollo de software. | |
| dc.format | 27 p. | |
| dc.format | application/pdf | |
| dc.language | es | |
| dc.publisher | Babahoyo: UTB-FAFI. 2023 | |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Ecuador | |
| dc.rights | http://creativecommons.org/licenses/by-nc-nd/3.0/ec/ | |
| dc.subject | Inteligencia artificial (IA) | |
| dc.subject | Code GPT | |
| dc.subject | GitHub Copilot | |
| dc.subject | Código | |
| dc.subject | Programación | |
| dc.title | Estudio comparativo de las herramientas de ia (inteligencia artificial) Code GPT Y GITHUB COPILOT como asistentes de programación en el desarrollo de software. | |
| dc.type | bachelorThesis | |
