Aplicación de técnicas de inteligencia artificial como método para generar código de programación en HTML y Python
Palabras clave:
Inteligencia artificial, generación de código, desarrollo de software, python, htmlResumen
Este artículo explora la aplicación de técnicas avanzadas de inteligencia artificial (IA) en la generación de código de programación, con un enfoque específico en los lenguajes Python y HTML. Utilizando modelos de IA como GPT-3 y Codex, el estudio evalúa la capacidad de estas herramientas para generar código funcional y eficiente en diferentes niveles de complejidad. Se diseñaron ejercicios de programación que abarcan niveles básico, medio y avanzado para probar la eficacia de la IA en la generación de código. La evaluación se centró en la compilación y ejecución del código generado por la IA, confirmándose su funcionalidad a través de su correcta ejecución. Los resultados revelan que, mientras la IA demuestra una competencia notable en generar código funcional y eficiente para tareas de complejidad baja a media, enfrenta desafíos en tareas más avanzadas que requieren un alto grado de creatividad y complejidad. El estudio también aborda las implicaciones de estos hallazgos para el desarrollo de software, destacando el potencial de la IA para mejorar la eficiencia y la calidad del software, así como las consideraciones éticas y las áreas para futuras investigaciones. Este trabajo proporciona una visión comprensiva de los avances actuales en la generación de código asistida por IA y su impacto potencial en el campo de la programación y el desarrollo de software.
Citas
Abasaheb, S. A., & Subashini, R. (2023). Enhancing HR efficiency through the integration of artificial intelligence and internet of things: A study on AI implementation in human resource management. ICST Transactions on Scalable Information Systems. https://doi.org/10.4108/eetsis.4208
Ali, J. M. (2023). AI-driven software engineering. Advances in Engineering Innovation, 3(1), 17–21. https://doi.org/10.54254/2977-3903/3/2023030
Alzyadat, W., Al-Madi, M., Muhairat, M., Al-Madi, N., & Altarawneh, F. (2023, August 9). A roadmap for artificial intelligence augmented software development life cycle: Aspects of knowledge vaporization. 2023 International Conference on Information Technology (ICIT). https://doi.org/10.1109/icit58056.2023.10226133
Arshad, H. B., Butt, S. A., Khan, S. U., Javed, Z., & Nasir, K. (2023). ChatGPT and artificial intelligence in hospital level research: Potential, precautions, and prospects. Methodist DeBakey Cardiovascular Journal, 19(5), 77–84. https://doi.org/10.14797/mdcvj.1290
Bajaj, Y., & Samal, M. K. (2023). Accelerating software quality: Unleashing the power of generative AI for automated test-case generation and bug identification. International Journal for Research in Applied Science and Engineering Technology, 11(7), 345–350. https://doi.org/10.22214/ijraset.2023.54628
Cheng, R., Wang, R., Zimmermann, T., & Ford, D. (2022). “It would work for me too”: How Online Communities Shape Software Developers’ Trust in AI-Powered Code Generation Tools. https://doi.org/10.48550/ARXIV.2212.03491
Choi, E., Leonard, K. W., Jassal, J. S., Levin, A. M., Ramachandra, V., & Jones, L. R. (2023). Artificial intelligence in facial plastic surgery: A review of current applications, future applications, and ethical considerations. Facial Plastic Surgery: FPS, 39(5), 454–459. https://doi.org/10.1055/s- 0043-1770160
Colther, C. (2021, November 15). The risks of AI and ethical considerations from the Human Scale Development approach. 2021 40th International Conference of the Chilean Computer Science Society (SCCC). https://doi.org/10.1109/sccc54552.2021.9650386
Devalla, S., & Yogix, M. K. (2023). Building trust in AI -A simplified guide to ensure software quality. Journal of Soft Computing Paradigm, 5(3), 218–231. https://doi.org/10.36548/jscp.2023.3.001
Doyal, A. S., Sender, D., Nanda, M., & Serrano, R. A. (2023). ChatGPT and artificial intelligence in medical writing: Concerns and ethical considerations. Cureus, 15(8), e43292. https://doi.org/10.7759/cureus.43292
Feng, Y., Vanam, S., Cherukupally, M., Zheng, W., Qiu, M., & Chen, H. (2023, June). Investigating code generation performance of ChatGPT with crowdsourcing social data. 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC). https://doi.org/10.1109/compsac57700.2023.00117
Hamza, M., Siemon, D., Akbar, M. A., & Rahman, T. (2023). Human AI collaboration in software engineering: Lessons learned from a hands on workshop. In arXiv [cs.SE]. http://arxiv.org/abs/2312.10620
Katirai, A. (2023). The ethics of advancing artificial intelligence in healthcare: analyzing ethical considerations for Japan’s innovative AI hospital system. Frontiers in Public Health, 11, 1142062. https://doi.org/10.3389/fpubh.2023.1142062
Kim, M., & Hong, B. (2022). Nursing ethical considerations in the AI-based technologies. J-Institute, 7(2), 10–21. https://doi.org/10.22471/ai.2022.7.2.10
Laskar, M. T. R., Bari, M. S., Rahman, M., Bhuiyan, M. A. H., Joty, S., & Huang, J. X. (2023). A systematic study and comprehensive evaluation of ChatGPT on benchmark datasets. https://doi.org/10.48550/ARXIV.2305.18486
Omar, R. (2023, March). AI And Energy Efficiency. 2023 IEEE 20th International Conference on Software Architecture Companion (ICSA-C). https://doi.org/10.1109/icsa-c57050.2023.00040
Parikh, N. A. (2023). Empowering business transformation: The positive impact and ethical considerations of generative AI in software product management -- A systematic literature review. https://doi.org/10.48550/ARXIV.2306.04605
Rao, D. (2023). The urgent need for healthcare workforce upskilling and ethical considerations in the Era of AI-assisted medicine. Indian Journal of Otolaryngology and Head and Neck Surgery: Official Publication of the Association of Otolaryngologists of India, 1–2. https://doi.org/10.1007/s12070- 023-03755-9
Reeves, B., Sarsa, S., Prather, J., Denny, P., Becker, B. A., Hellas, A., Kimmel, B., Powell, G., & Leinonen, J. (2023, June 29). Evaluating the performance of code generation models for solving parsons problems with small prompt variations. Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1. ITiCSE 2023: Innovation and Technology in Computer Science Education, Turku Finland. https://doi.org/10.1145/3587102.3588805
Sikand, S., Sharma, V. S., Kaulgud, V., & Podder, S. (2023, September 11). Green AI Quotient: Assessing Greenness of AI-based software and the way forward. 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE). https://doi.org/10.1109/ase56229.2023.00115
Waseem, M., Das, T., Ahmad, A., Fehmideh, M., Liang, P., & Mikkonen, T. (2023). Using ChatGPT throughout the software development life cycle by novice developers. https://doi.org/10.48550/ARXIV.2310.13648
