FIIADS: un framework holístico para la implementación de inteligencia artificial en el desarrollo de software

Marcela Paladino; Claudia Pons

Autores/as

Marcela Paladino Universidad Nacional de La Plata, Argentina https://orcid.org/0000-0003-1149-0976
Claudia Pons Universidad Nacional de La Plata, Universidad Abierta Interamericana, Comisión de Investigaciones Científicas de la Provincia de Buenos Aires, Argentina https://orcid.org/0009-0007-4406-9594

Palabras clave:

inteligencia artificial, ciclo de vida del desarrollo de software, framework, ingeniería de software, gobernanza de IA

Resumen

La incorporación de la inteligencia artificial (IA) en el ciclo de vida del desarrollo de software (Software Development Life Cycle, SDLC) se ha intensificado en los últimos años, impulsada por avances en aprendizaje automático y, especialmente, por la expansión de modelos generativos. Aunque estas tecnologías ofrecen oportunidades para mejorar la productividad, la automatización y la calidad del software, su adopción efectiva continúa enfrentando desafíos técnicos, organizacionales y éticos que suelen abordarse de manera fragmentada en la literatura. En este contexto, este trabajo propone FIIADS (Framework de Implementación de Inteligencia Artificial en el Desarrollo de Software), un framework holístico orientado a guiar la incorporación de IA a lo largo del SDLC de forma sistemática, responsable y evaluable. La investigación se desarrolló bajo el paradigma Design Science Research (DSR), a partir de una revisión crítica del estado del arte y del diseño de un artefacto conceptual articulado en tres dimensiones: técnica, organizacional y ética. El framework incluye procesos, métricas y artefactos que permiten orientar la implementación de IA en distintas fases del SDLC y en diversos contextos organizacionales. La validación se realizó a nivel conceptual mediante la comparación con frameworks existentes, la aplicación a casos documentados en la literatura y el análisis de coherencia interna. Los resultados sugieren que FIIADS ofrece una base estructurada y adaptable para la implementación gradual, responsable y evaluable de IA en organizaciones de desarrollo de software.

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Referencias

Ali, A. & Gravino, C. (2019). A systematic literature review of software effort prediction using machine learning methods. Journal of Software: Evolution and Process, 31(10), e2211. https://doi.org/10.1002/smr.2211

Amershi, S., Begel, A., Bird, C., DeLine, R., Gall, H., Kamar, E., et al. (2019). Software engineering for machine learning: A case study. Proceedings of the 41st International Conference on Software Engineering, 291–300. https://doi.org/10.1109/ICSESEIP.2019.00042

Breck, E., Cai, S., Nielsen, E., Salib, M., & Sculley, D. (2017). The ML test score: A rubric for ML production readiness and technical debt reduction. Proceedings of the IEEE International Conference on Big Data, 1123–1132. https://doi.org/10.1109/BigData.2017.8258038

Cheng, H., Husen, J. H., Lu, Y., Racharak, T., Yoshioka, N., Ubayashi, N., & Washizaki, H. (2026). Generative AI for Requirements Engineering: A systematic literature review. Software: Practice and Experience, 56(2), 141–170. https://doi.org/10.1002/spe.70029

Dignum, V. (2019). Responsible artificial intelligence: How to develop and use AI in a responsible way. Springer. https://doi.org/10.1007/978-3-030-30371-6

European Commission High-Level Expert Group on Artificial Intelligence. (2019). Ethics guidelines for trustworthy AI. European Commission.

European Union. (2024). Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act).

Fan, A., Gokkaya, B., Harman, M., Lyubarskiy, M., Sengupta, S., Yoo, S., & Zhang, J. M. (2023). Large language models for software engineering: Survey and open problems. arXiv preprint arXiv:2310.03533. https://doi.org/10.1109/ICSE-FoSE59343.2023.00008

Ferrari, A., Gori, G., Rosadini, B., Trotta, I., Bacherini, S., Fantechi, A., & Gnesi, S. (2018). Detecting requirements defects with NLP patterns: An industrial experience in the railway domain. Empirical Software Engineering, 23(6), 3684–3733. https://doi.org/10.1007/s10664-018-9596-7

Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., et al. (2018). AI4People-An ethical framework for a good AI society. Minds and Machines, 28(4), 689-707. https://doi.org/10.1007/s11023-018-9482-5

Fu, Y., Liang, P., Tahir, A., Li, Z., Shahin, M., Yu, J., & Chen, J. (2025). Security weaknesses of Copilot-generated code in GitHub projects: An empirical study. ACM Transactions on Software Engineering and Methodology. https://doi.org/10.1145/3716848

Grant, M. J., & Booth, A. (2009). A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information & Libraries Journal, 26(2), 91–108. https://doi.org/10.1111/j.1471-1842.2009.00848.x

Gregor, S., & Hevner, A. R. (2013). Positioning and presenting design science research for maximum impact. MIS Quarterly, 37(2), 337–355. https://doi.org/10.25300/MISQ/2013/37.2.01

Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105. https://doi.org/10.2307/25148625

IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems. (2020). Ethically aligned design: A vision for prioritizing human well-being with autonomous and intelligent systems (2nd ed.). IEEE Standards Association.

International Organization for Standardization. (2023). ISO/IEC 42001:2023. Artificial intelligence - Management system.

Jabareen, Y. (2009). Building a conceptual framework: Philosophy, definitions, and procedure. International Journal of Qualitative Methods, 8(4), 49–62. https://doi.org/10.1177/160940690900800406

Kamei, Y., Shihab, E., Adams, B., Hassan, A. E., Mockus, A., Sinha, A., & Ubayashi, N. (2013). A large-scale empirical study of just-in-time quality assurance. IEEE Transactions on Software Engineering, 39(6), 757–773. https://doi.org/10.1109/TSE.2012.70

Kreuzberger, D., Kühl, N., & Hirschl, S. (2023). Machine Learning Operations (MLOps): Overview, definition, and architecture. IEEE Access, 11, 31866–31879. https://doi.org/10.1109/ACCESS.2023.3262138

Liao, Q. V., & Wortman Vaughan, J. (2024). AI transparency in the age of LLMs: A humancentered research roadmap. Harvard Data Science Review, Special Issue 5. https://doi.org/10.1162/99608f92.8036d03b

National Institute of Standards and Technology. (2023). Artificial intelligence risk management framework (AI RMF 1.0). U.S. Department of Commerce.

Paladino, M., & Pons, C. (2025). From Obstacles to Strategies: Integrating Artificial Intelligence into the Software Development Life Cycle. 54JAIIO. Jornadas Argentinas de Informática, 11(2), 101–117.

Paladino, M., & Pons, C. (2026). Material complementario de FIIADS: un framework holístico para la implementación de inteligencia artificial en el desarrollo de software (documento de especificación). (Versión: 1.0 - 25/6/2026). Zenodo. https://doi.org/10.5281/zenodo.20977531

Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3), 45–77. https://doi.org/10.2753/MIS0742-1222240302

Peng, S., Kalliamvakou, E., Cihon, P., & Demirer, M. (2023). The impact of AI on developer productivity: Evidence from GitHub Copilot. arXiv:2302.06590.

Pospieszny, P., Czarnacka-Chrobot, B., & Kobylinski, A. (2018). An effective approach for software project effort and duration estimation with machine learning algorithms. Journal of Systems and Software, 137, 184–196. https://doi.org/10.1016/j.jss.2017.11.066

Russo, D. (2024). Navigating the complexity of generative AI adoption in software engineering. ACM Transactions on Software Engineering and Methodology. https://doi.org/10.1145/3652154

Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., et al. (2015). Hidden technical debt in machine learning systems. Advances in Neural Information Processing Systems, 28, 2503–2511.

Siddiq, M. L., Santos, J. C. S., Tanvir, R. H., Ulfat, N., Al Rifat, F., & Lopes, V. C. (2024). Using large language models to generate JUnit tests: An empirical study. Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering, 313–322. https://doi.org/10.1145/3661167.3661216

Silva, R. K., Farias, K., Kunst, R., & Dalzochio, J. (2023). An approach based on machine learning for predicting software design problems. Proceedings of the XIX Brazilian Symposium on Information Systems. https://doi.org/10.1145/3592813.3592888

Tornatzky, L., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books.

UNESCO. (2021). Recommendation on the ethics of artificial intelligence. United Nations Educational, Scientific and Cultural Organization.

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926

Whetten, D. A. (1989). What constitutes a theoretical contribution? Academy of Management Review, 14(4), 490–495. https://doi.org/10.2307/258554

Wieringa, R. (2014). Design science methodology for information systems and software engineering. Springer. https://doi.org/10.1007/978-3-662-43839-8

Zhang, Q., Fang, C., Xie, Y., Zhang, Y., Yang, Y., Sun, W., Yu, S., & Chen, Z. (2023). A survey on large language models for software engineering. arXiv:2312.15223.

Zhao, L., Alhoshan, W., Ferrari, A., Letsholo, K. J., Ajagbe, M. A., Chioasca, E.-V., & BatistaNavarro, R. T. (2021). Natural language processing for requirements engineering: A systematic mapping study. ACM Computing Surveys, 54(3), Article 55. https://doi.org/10.1145/3444689