De la algoritmia clásica a la inteligencia generativa: la IA como nuevo paradigma en la ingeniería de software

Juan Rigoberto  Castillo Serracín; Javier Miguel  Gómez S.

doi:10.48204/contacto.v6n1.11292

Submitted September 20, 2026

Published 2026-09-23

Artículos académicos

Vol. 6 No. 1 (2026): Revista Contacto

From classical algorithms to generative intelligence: AI as a new paradigm in software engineering

Juan Rigoberto Castillo Serracín⁺⁻
Javier Miguel Gómez S. ⁺⁻

DOI https://doi.org/10.48204/contacto.v6n1.11292

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References

DOI: 10.48204/contacto.v6n1.11292

Published: 2026-09-23

How to Cite

Castillo Serracín, J. R., & Gómez S. , J. M. (2026). From classical algorithms to generative intelligence: AI as a new paradigm in software engineering. Revista Contacto, 6(1), 26–43. https://doi.org/10.48204/contacto.v6n1.11292

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Abstract

This research analyzes the epistemological rupture in software engineering caused by the transition from the traditional deterministic paradigm towards probabilistic models based on Generative Artificial Intelligence. Through a structured bibliographic review (2021-2026) and triangulation of industrial data, the obsolescence of manual coding in general-purpose applications is examined, contrasted with its irreducible validity in Safety-Critical Systems. The results evidence that the current assisted model increases productivity but introduces significant security risks and technical debt due to automation bias and context limitations. A new Systemic Generative Engineering Model is proposed, based on massive context windows and multimodal reasoning, concluding that the engineer's role must inescapably evolve from syntactic writing towards the architecture and auditing of stochastic systems under a mandatory human-in-the-loop framework.

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References

CISA. (2023). Shifting the balance of cybersecurity risk: Principles and priorities for security-by-design and default. Cybersecurity & Infrastructure Security Agency. https://www.cisa.gov/resources-tools/resources/secure-by-design
Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. d. O., Kaplan, J., ... & Zaremba, W. (2021). Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374. https://doi.org/10.48550/arXiv.2107.03374
Dohmke, T., Dellangnol, I., & Pulley, C. (2023). Sea change: The future of AI and software development. GitHub. https://github.blog/2023-06-27-the-economic-impact-of-the-ai-powered-developer-lifecycle-and-lessons-from-github-copilot/
GitHub. (2024). The state of the octoverse 2024: AI and the evolution of the developer workflow. GitHub News. https://github.blog/news-insights/octoverse/octoverse-2024/
GitHub. (2024). The state of the Octoverse: 2024 report on open source software. GitHub Resources. https://github.blog/octoverse/
GitClear. (2024). Coding on Copilot: 2023 data suggests downward pressure on code quality. https://www.gitclear.com/coding_on_copilot_data_shows_ais_downward_pressure_on_code_quality
Hou, X., Zhao, Y., Liu, Y., Yang, Z., Wang, K., Li, L., ... & Lo, D. (2024). Large language models for software engineering: A systematic literature review. arXiv preprint arXiv:2308.10620. https://doi.org/10.48550/arXiv.2308.10620
IBM Security. (2025). X-Force Threat Intelligence Index 2025: The impact of generative AI on cyber resilience. IBM Corporation. https://www.ibm.com/reports/threat-intelligence
Knight, J. C. (2002). Safety-critical systems: Challenges and directions. Proceedings of the 24th International Conference on Software Engineering (ICSE), 547-550. https://doi.org/10.1145/581339.581406
Lakhani, K. R., & Sull, D. (2024). The era of the AI-augmented engineer: Beyond syntactic coding. Harvard Business Review. https://hbr.org/2024/02/how-ai-is-changing-the-way-developers-work
McKinsey & Company. (2023). Unleashing developer productivity with generative AI. McKinsey Digital. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/unleashing-developer-productivity-with-generative-ai
Mozannar, H., Bansal, G., Fourney, A., & Horvitz, E. (2024). Reading between the lines: Modeling user behavior and costs in AI-assisted programming. arXiv preprint arXiv:2210.14306.
Pearce, H., Ahmad, B., Tan, B., Dolan-Gavitt, B., & Karri, R. (2022). Asleep at the keyboard? Assessing the security of GitHub Copilot’s code contributions. 2022 IEEE Symposium on Security and Privacy (SP), 754-768. https://arxiv.org/abs/2108.09293
Peng, S., Kalliamvakou, E., Cihon, P., & Demarrer, M. (2023). The impact of AI on developer productivity: Evidence from GitHub Copilot. arXiv preprint arXiv:2302.06590.
Perry, N., Srivastava, M., Kumar, D., & Boneh, D. (2023). Do users write more insecure code with AI assistants? Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security, 2785–2799. https://doi.org/10.1145/3548606.3560605
Reid, M., Savinov, N., Teplyashin, D., & Co-authors (Google DeepMind). (2024). Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. Google DeepMind Technical Report.
Russo, D., Laredo, J. L., Storey, M. A., Hoda, R., Serebrenik, A., & Pandita, R. (2024). Generative AI in software engineering must be human-centered: The Copenhagen Manifesto. Journal of Systems and Software, 216, 112115. https://dl.acm.org/doi/10.1016/j.jss.2024.112115
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.