A software artifact for the rapid optimization of llms in the  software development life cycle

Vania Linette Méndez Morales; José Manuel Gómez Zea; José Ángel Jesús Magaña; Alejandro Hernández Cadena; Teresa de Jesús Javier Baeza

doi:10.61117/ipsumtec.v9i1.457

Authors

Vania Linette Méndez Morales National Technological Institute of Mexico https://orcid.org/0009-0003-5303-1898
José Manuel Gómez Zea National Technological Institute of Mexico https://orcid.org/0000-0001-7474-5601
José Ángel Jesús Magaña National Technological Institute of Mexico https://orcid.org/0000-0002-3010-6661
Alejandro Hernández Cadena National Technological Institute of Mexico
Teresa de Jesús Javier Baeza National Technological Institute of Mexico https://orcid.org/0009-0007-7483-7072

DOI:

https://doi.org/10.61117/ipsumtec.v9i1.457

Keywords:

Artefacto, desarrollo de software, herramientas, indicaciones, inteligencia artificial, programadores

Abstract

La inteligencia artificial (IA) desempeña un papel fundamental en el desarrollo de software moderno, transformando significativamente el diseño, la escritura, las pruebas y el mantenimiento del código por parte de los desarrolladores. En la actualidad, programadores de distintos niveles han integrado herramientas basadas en IA en diferentes fases del ciclo de vida del desarrollo de software (SDLC), desde la generación de código hasta la implementación. Este estudio analiza el impacto de estas tecnologías en la práctica profesional, identifica las herramientas más utilizadas y propone las mejores prácticas para la adopción responsable de la IA, con el objetivo de optimizar su implementación de manera eficiente y ética. Como parte de este estudio, se desarrolló un artefacto metodológico para guiar la formulación estructurada de indicaciones, que funciona como un modelo para mejorar la precisión y la utilidad de los resultados generados por la IA. Este artefacto se validó a través de tres casos de uso de prueba de concepto (consultas SQL, desarrollo de backend e implementación en AWS), lo que demostró su potencial como base de conocimientos para los equipos que buscan incorporar herramientas de IA de forma sistemática en sus flujos de trabajo.

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Author Biographies

Vania Linette Méndez Morales, National Technological Institute of Mexico

Vania Linette Méndez Morales has a degree in Computer Systems Engineering from the Tecnológico Nacional de México, Campus Villahermosa (2023), and is currently pursuing a Master's Degree in Information Technology at the same institution. She is part of the academic community of the Department of Systems and Computing and her fields of interest include databases, information analysis, programming and emerging technologies.

José Manuel Gómez Zea , National Technological Institute of Mexico

José Manuel Gómez-Zea is a PhD in Technological Development from the Descartes University of science and a master's degree in computer technologies from the Mundo Maya University. He works as a Professor at the National Institute of Technology of Mexico (TECNM) Villahermosa campus in the Department of Systems and Computing, His field of research is Software Engineering, Artificial Intelligence, and Industrial Control Applications through the Internet of Things.

José Ángel Jesús Magaña, National Technological Institute of Mexico

José Ángel Jesús-Magaña He received his degree in Computer Systems Engineering and his Master's degree in Computer Systems from the Instituto Tecnológico de Mérida; and his PhD in Technological Development from the Descartes University of Science and Technology. He is currently a Professor in the Department of Systems and Computing and is the Software Development Coordinator at the Computing Center of the National Technological Institute of Mexico, Villahermosa campus.

Alejandro Hernández Cadena, National Technological Institute of Mexico

Alejandro Hernández Cadena holds a PhD in Technological Development from Descartes University of Science and Technology (Tuxtla Gutiérrez, Chiapas, 2020), a Master's degree in Computer Technologies from Mundo Maya University, and a Bachelor's degree in Computer Science from the Technological Institute of Villahermosa.

Teresa de Jesús Javier Baeza , National Technological Institute of Mexico

Teresa de Jesús Javier Baeza is a Mexican academic with a solid background in education and technology. She holds a bachelor's degree in Computer Science (1993), a master's degree in Information Technology Management (2008) and a doctorate in Education (2012). She currently teaches at the Tecnológico Nacional de México, Campus Villahermosa, in the Department of Systems and Computing.

References

J. M. Martínez Gómez, M. E. Higuera Marín, Y E. Aguilar Díaz, «Enfoque Metodológico Para El Diseño De Interfaces Durante El Ciclo De Vida De Desarrollo De Software», Gti, Vol. 12, N.º 34, Pp. 59–73, Feb. 2014

S, Shylesh, A Study of Software Development Life Cycle Process Models (June 10, 2017). Available at SSRN: https://ssrn.com/abstract=2988291 or http://dx.doi.org/10.2139/ssrn.2988291

Treude C, Storey MA. Generative AI and Empirical Software Engineering: A Paradigm Shift. 2025 Feb 12 [cited 2025 Oct 18]; Available from: https://arxiv.org/pdf/2502.08108

P. Maddigan and T. Susnjak, "Chat2VIS: Generating Data Visu-alizations via Natural Language Using ChatGPT, Codex and GPT-3 Large Language Models," in IEEE Access, vol. 11, pp. 45181-45193, 2023, doi: 10.1109/ACCESS.2023.3274199.

J. M. Martínez Gómez, M. E. Higuera Marín, Y E. Aguilar Díaz, «Enfoque Metodológico Para El Diseño De Interfaces Durante El Ciclo De Vida De Desarrollo De Software», Gti, Vol. 12, N.º 34, Pp. 59–73, Feb. 2014

S, Shylesh, A Study of Software Development Life Cycle Process Models (June 10, 2017). Available at SSRN: https://ssrn.com/abstract=2988291 or http://dx.doi.org/10.2139/ssrn.2988291 DOI: https://doi.org/10.2139/ssrn.2988291

Treude C, Storey MA. Generative AI and Empirical Software Engineering: A Paradigm Shift. 2025 Feb 12 [cited 2025 Oct 18]; Available from: https://arxiv.org/pdf/2502.08108

P. Maddigan and T. Susnjak, "Chat2VIS: Generating Data Visu-alizations via Natural Language Using ChatGPT, Codex and GPT-3 Large Language Models," in IEEE Access, vol. 11, pp. 45181-45193, 2023, doi: 10.1109/ACCESS.2023.3274199. DOI: https://doi.org/10.1109/ACCESS.2023.3274199

Y. Luo, N. Tang, G. Li, J. Tang, C. Chai and X. Qin, "Natural Language to Visualization by Neural Machine Translation," in IEEE Transactions on Visualization and Computer Graphics, vol. 28, no. 1, pp. 217-226, Jan. 2022, doi: 10.1109/TVCG.2021.3114848. DOI: https://doi.org/10.1109/TVCG.2021.3114848

Alenezi M, Akour M. AI-Driven Innovations in Software Engi-neering: A Review of Current Practices and Future Directions. Applied Sciences 2025, Vol 15, Page 1344 [Internet]. 2025 Jan 28 [cited 2025 Oct 18];15(3):1344. Available from: https://www.mdpi.com/2076-3417/15/3/1344/htm DOI: https://doi.org/10.3390/app15031344

Bejarano MH, Baquero-Rey LE, Bejarano MH, Baquero-Rey LE. AI-Driven Software Development: A Paradigm Shift in Software Engineering. 2025 May 27 [cited 2025 Oct 18]; Available from: https://www.intechopen.com/chapters/1206853

Marar HW. Advancements in software engineering using AI. Computer Software and Media Applications [Internet]. 2024 Feb 1 [cited 2025 Oct 18];6(1):3906. Available from: https://systems.enpress-publisher.com/index.php/CSMA/article/view/3906 DOI: https://doi.org/10.24294/csma.v6i1.3906

Chen, M., Tworek, J., Jun, H., Yuan, Q., De Oliveira Pinto, H. P., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., Ray, A., Puri, R., Krueger, G., Petrov, M., Khlaaf, H., Sastry, G., Mishkin, P., Chan, B., Gray, S., . . . Zaremba, W. (2021, 7 julio). Evaluating Large Language Models Trained on Code. arXiv.org. https://arxiv.org/abs/2107.03374

Zhang, B., Liang, P., Zhou, X., Ahmad, A., & Waseem, M. (2023). Practices and Challenges of Using GitHub Copilot: An Empirical Study. Proceedings/Proceedings Of The . . . International Con-ference On Software Engineering And Knowledge Engineering, 2023, 124-129. https://doi.org/10.18293/seke2023-077 DOI: https://doi.org/10.18293/SEKE2023-077

Song, D., Zhou, Z., Wang, Z., Huang, Y., Chen, S., Kou, B., ... & Zhang, T. (2023). An empirical study of code generation errors made by large language models. In 7th Annual Symposium on Machine Programming.

Papers, O. D. (22 de 02 de 2022). OECD Framework for the Classification of AI systems. Recuperado el 02 de 05 de 2025, de https://www.oecd.org/en/publications/oecd-framework-for-the-classification-of-ai-systems_cb6d9eca-en.html

Soni, Arpita and Kumar, Anoop and Arora, Rajeev and Garine, Ramakrishna, Integrating AI into the Software Development Life Cycle: Best Practices, Tools, and Impact Analysis (June 10, 2023). Available at SSRN: https://ssrn.com/abstract=4918992 or http://dx.doi.org/10.2139/ssrn.4918992 DOI: https://doi.org/10.2139/ssrn.4918992

Marcillo, F., Castillo Anzules, M. S., & Begnini, L. (2024). Heu-rística aplicada en inteligencia artificial, una revisión sistemáti-ca. Revista Científica Kosmos, 3(2), 81–94. https://doi.org/10.62943/rck.v3n2.2024.100 DOI: https://doi.org/10.62943/rck.v3n2.2024.100

N. A. Sajja, N. D. Thakur, y N. A. Mehra, «Integrating Generative AI into the Software Development Lifecycle: Impacts on Code Quality and Maintenance», International Journal Of Science And Research Archive, vol. 13, n.o 1, pp. 1952-1960, oct. 2024, doi: 10.30574/ijsra.2024.13.1.1837. DOI: https://doi.org/10.30574/ijsra.2024.13.1.1837

Villasís-Keever, M. Á., & Miranda-Novales, M. G. (2016). El protocolo de investigación IV: las variables de estudio. Deleted Journal, 63(3), 303-310. https://doi.org/10.29262/ram.v63i3.199 DOI: https://doi.org/10.29262/ram.v63i3.199

Oyola-García, Alfredo Enrique. (2021). La variable. Revista del Cuerpo Médico Hospital Nacional Almanzor Aguinaga Asen-jo, 14(1), 90-93. https://doi.org/10.35434/rcmhnaaa.2021.141.905 DOI: https://doi.org/10.35434/rcmhnaaa.2021.141.905

Hernandez Mendoza, S., & Duana Avila, D. (2020). Técnicas e instrumentos de recolección de datos. Boletín Científico De Las Ciencias Económico Administrativas Del ICEA, 9(17), 51–53. https://doi.org/10.29057/icea.v9i17.6019 DOI: https://doi.org/10.29057/icea.v9i17.6019

Hamed Taherdoost. Data Collection Methods and Tools for Re-search; A Step-by-Step Guide to Choose Data Collection Technique for Academic and Business Research Projects. International Journal of Academic Research in Management (IJARM), 2021, 10 (1), pp.10-38. ⟨hal-03741847⟩

A. Borgobello, M. P. Pierella, and M.-I. Pozzo, “Ús de qüestionaris en recerca sobre la universitat: anàlisi d’experiències amb una perspectiva situada”, REIRE, vol. 12, no. 2, pp. 1–16, Jul. 2019.

Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Po-losukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, Slav Petrov; Natural Questions: A Benchmark for Question Answering Research. Transactions of the Association for Computational Linguistics 2019; 7 453–466. doi: https://doi.org/10.1162/tacl_a_00276 DOI: https://doi.org/10.1162/tacl_a_00276

Murat, P. (2020). Analysis of Multiple-choice versus Open-ended Questions in Language Tests According to Different Cognitive Domain Levels. Novitas - Journal Research on Youth and Lan-guage, 14(2), 76-96.

Fullstory. (03 de December de 2021). What is quantitative data? How to collect, understand, and analyze it. Obtenido de Fullstory BLOG: https://www.fullstory.com/blog/quantitative-data

Leyva López, Hermelinda Patricia, Pérez Vera, Monserrat Gabriela, & Pérez Vera, Sandra Mercedes. (2018). Google Forms en la evaluación diagnóstica como apoyo en las actividades docentes. Caso con estudiantes de la Licenciatura en Turismo. RIDE. Revista Iberoamericana para la Investigación y el Desarrollo Educati-vo, 9(17), 84-111. https://doi.org/10.23913/ride.v9i17.374 DOI: https://doi.org/10.23913/ride.v9i17.374

Pillajo Sotalín, Adriana Jovita (2019) Guía Digital Del Uso Del Formulario De Google Forms Para La Evaluación En Básica Su-perior Quito Uisrael, Maestría En Educación Mención: Gestión Del Aprendizaje Por Tic Quito: Universidad Israel 2019, 127p. PhD. PhD. RENÉ CORTIJO UIS-RAEL-EC-MASTER-EDU-378-242-2019-012

Mora Naranjo, B. M., Aroca Izurieta, C. E., Tiban Leica, L. R., ánchez Morrillo, C. F., & Jiménez Salazar, A. (2023). Ética y Responsabilidad en la Implementación de la Inteligencia Artificial en la Educación. Ciencia Latina. Revista Multidisciplinar, 7(6), 2054 - 2076. doi:https://doi.org/10.37811/cl_rcm.v7i6.8833 DOI: https://doi.org/10.37811/cl_rcm.v7i6.8833

Owusu, P., Raef, A., & Sharaf, E. (2025). Carbonate Seismic Facies Analysis in Reservoir Characterization: A Machine Learning Ap-proach with Integration of Reservoir Mineralogy and Porosi-ty. Geosciences, 15(7), 257. https://doi.org/10.3390/geosciences15070257 DOI: https://doi.org/10.3390/geosciences15070257

Barney, N. (26 de August de 2024). What is language modeling? Obtenido de TechTarget: https://www.techtarget.com/searchenterpriseai/definition/language-modeling

Menzinsky, A. L., Palacio, J., & Sobrino, M. A. (Agosto de 2022). Historias de Usuario, Ingeniería de Requisitos Ágil. (S. Manager, Ed.) Recuperado el 25 de 04 de 2025, de SafeCreative: https://www.safecreative.org/work/2009135322450?0

J. M. Gómez-Zea, J. Ángel Jesús-Magaña, J. de la Cruz-Alvarez, E. Morales-Romero, y E. Sosa-Silva, «Optimización de la docu-mentación en proyectos de software ágiles: Buenas prácticas y ar-tefactos en el marco de trabajo SCRUM», ProgMat, vol. 15, n.º 3, pp. 51–64, nov. 2023. DOI: https://doi.org/10.30973/progmat/2023.15.3/5

K. K. Ruiz Mendoza, «El uso de ChatGPT 4.0 para la elaboración de exámenes: crear el prompt adecuado: The Use of ChatGPT 4.0 for Test Development: Creating the Right Prompt », LATAM, vol. 4, n.º 2, pp. 6142–6157, ago. 2023. DOI: https://doi.org/10.56712/latam.v4i2.1040

Islam Sakib, M. S. (February de 2023). What is ChatGPT? Obte-nido de researchGate: https://www.researchgate.net/publication/367794587_What_is_ChatGPT

Ortiz, P., & Ortiz, P. (2025, 22 mayo). Chat GPT: qué es, para qué sirve y su aplicación en la economía [explicado por Chat GPT]. EDEM Escuela de Empresa-rios. https://edem.eu/chat-gpt-que-es-para-que-sirve-y-su-aplicacion-en-la-economia-explicado-por-chat-gpt/

M. E. Chávez Solís, E. Labrada Martínez, E. Carbajal Degante, E. Pineda Godoy, y Y. Alatristre Martínez, «Inteligencia artificial ge-nerativa para fortalecer la educación superior: Generative artificial intelligence to boost higher education», LATAM, vol. 4, n.º 3, pp. 767–784, sep. 2023. DOI: https://doi.org/10.56712/latam.v4i3.1113

Belcic, I., & Stryker, C. (2025, 22 mayo). ChatGPT. IBM. https://www.ibm.com/mx-es/think/topics/chatgpt

Bonteanu, A.M.; Tudose, C. Performance Analysis and Im-provement for CRUD Operations in Relational Databases from Java Programs Using JPA, Hibernate, Spring Data JPA. Appl. Sci. 2024, 14, 2743. https://doi.org/10.3390/app14072743 DOI: https://doi.org/10.3390/app14072743

Méndez Morales, V. L.; Gómez Zea, J. M.; Jesús Magaña, J. Á.; Javier Baeza, T. D. J.; Hernández Cadena, A.; de la Cruz Álvarez, J. Model for the Adoption of AI Tools in the Software Devel-opment Life Cycle: A Framework for Prompt Optimization in LLMs. Preprints 2025, 2025091036. https://doi.org/10.20944/preprints202509.1036.v1 DOI: https://doi.org/10.20944/preprints202509.1036.v1