Grandes Modelos Lingüísticos en la Ingeniería de Requerimientos: Una Revisión de la Literatura
Resumen
There are several proposals in the literature on the application of Natural Language Processing – NLP to address activities and challenges in requirements engineering – RE. In recent years, Generative Artificial Intelligence – AI implemented with Large Language Models – LLM has gained great recognition due to the improvements contributed to NLP tasks. This work proposes a systematic literature review – SLR to collect research that presents some use of LLMs to solve problems and improve the RE process. The results show promising proposals aimed mainly at different model creation and requirements classification tasks. However, these proposals need more development and empirical validation to be widely accepted and applied in software development environments. Therefore, it is necessary to continue researching the applications of LLM in the RE process.
Referencias bibliográficas
Abualhaija, S., Arora, C., Sleimi, A., & Briand, L. C. (2022). Automated question Answering for improved understanding of compliance requirements: A multi-document study. 2022 IEEE 30th International Requirements Engineering Conference (RE), 15-19 August 2022, Melbourne, Australia (págs. 39 - 50). IEEE. https://doi.org/10.1109/RE54965.2022.00011
Arulmohan, S., Meurs, M.-J., & Mosser, S. (2023). Extracting domain models from textual requirements in the era of large language models. 2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C) (págs. 580 - 587). Västerås, Suecia: IEEE Press. https://doi.org/10.1109/MODELS-C59198.2023.00096
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., . . . Liang, P. (12 de Julio de 2022). Onthe opportunities and risks of foundation models. arXiv, 2108.07258v3 [cs.LG], 1-214. https://doi.org/10.48550/arXiv.2108.07258
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., . . . Amodei, D. (2020). Language models are few-shot learners. En H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Ed.), Advances in Neural Information Processing Systems, 34th Conference on Neural Information Processing Systems (NeurIPS 2020). 33, págs. 1877 - 1901. Vancouver, Canada: Curran Associates Inc. Obtenido de https://proceedings.neurips.cc/paper_files/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf
Chatterjee, R., Ahmed, A., Anish, P. R., Suman, B., Lawhatre, P., & Ghaisas, S. (2021). A pipeline for automating labeling to prediction in classification of NFRs. 2021 IEEE 29th International Requirements Engineering Conference (RE), 20-24 September 2021, Notre Dame, IN, USA (págs. 323 - 323). IEEE. https://doi.org/10.1109/RE51729.2021.00036
Chen, B., Chen, K., Hassani, S., Yang, Y., Amyot, D., Lessard, L., . . . Varró, D. (2023). On the use of GPT-4 for creating goal models: An exploratory study. 2023 IEEE 31st International Requirements Engineering Conference Workshops (REW), 04-05 September 2023, Hannover, Alemania (págs. 262 - 271). IEEE. https://doi.org/10.1109/REW57809.2023.00052
Cosler, M., Hahn, C., Mendoza, D., Schmitt, F., & Trippel, C. (2023). nl2spec: Interactively translating unstructured natural language to temporal logics with large language models. En C. Enea, & A. Lal (Ed.), Computer Aided Verification. CAV 2023. Lecture Notes in Computer Science, vol. 13965 (págs. 383 - 396). Suiza: Springer Nature. https://doi.org/10.1007/978-3-031-37703-7_18
Cruciani, F., Moore, S., & Nugent, C. (2023). Comparing general purpose pre-trained word and sentence embeddings for requirements classification. En A. Ferrari, B. Penzenstadler, I. Hadar, S. Oyedeji, S. Abualhaija, A. Vogelsang, . . . D. Amyot (Ed.), Joint Proceedings of REFSQ-2023 Workshops, Doctoral Symposium, Posters & Tools Track, and Journal Early Feedback Track. Co-located with REFSQ 2023, April 17. 3378. Barcelona, Cataluña, España: CEUR-WS.org. Obtenido de https://ceur-ws.org/Vol-3378/NLP4RE-paper4.pdf
DAIR.AI. (23 de Abril de 2023). Elements of a Prompt. Obtenido de Prompt Engineering Guide: https://www.promptingguide.ai/introduction/elements
De Vito, G., Palomba, F., Gravino, C., Di Martino, S., & Ferrucci, F. (2023). ECHO: An approach to enhance use case quality exploiting large language models. 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), 06-08 September 2023 (págs. 53 - 60). Durres, Albania: IEEE. https://doi.org/10.1109/SEAA60479.2023.00017
Devine, P., Koh, Y. S., & Blincoe, K. (2023). Evaluating software user feedback classifier performance on unseen apps, datasets, and metadata. Empirical Software Engineering, 28, 26. https://doi.org/10.1007/s10664-022-10254-y
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. En J. Burstein, C. Doran, & T. Solorio (Ed.), Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 1, págs. 4171 - 4186. Minneapolis, Minnesota, USA: Association for Computational Linguistics. https://doi.org/10.18653/v1/N19-142
Ezzini, S., Abualhaija, S., Arora, C., & Sabetzadeh, M. (2022). Automated handling of anaphoric ambiguity in requirements: A multi-solution study. 44th IEEE/ACM 44th International Conference on Software Engineering, {ICSE} 2022, May 25-27 (págs. 187 - 199). Pittsburgh, PA, USA: ACM. https://doi.org/10.1145/3510003.3510157
Ezzini, S., Abualhaija, S., Arora, C., & Sabetzadeh, M. (2023). AI-based question answering assistance for analyzing natural-language requirements. 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE), 14-20 May 2023 (págs. 1277 - 1289). Melbourne, Australia: IEEE. https://doi.org/10.1109/ICSE48619.2023.00113
Fengli, X., Hao, Q., Shao, C., Zong, Z., Li, Y., Wang, J., . . . Yong, L. (10 de Octubre de 2025). Toward large reasoning models: A survey of reinforced reasoning with large language models. Patterns, 6(10), 1 - 30, 101370. https://doi.org/10.1016/j.patter.2025.101370
Görer, B., & Aydemir, F. B. (2023). Generating requirements elicitation interview scripts with large language models. 2023 IEEE 31st International Requirements Engineering Conference Workshops (REW). 04-05 September 2023 (págs. 44 - 51). Hannover, Alemania: IEEE. https://doi.org/10.1109/REW57809.2023.00015
Gramajo, M. G., Ballejos, L., & Ale, M. (Julio de 2020). Seizing requirements engineering issues through supervised learning techniques. IEEE Latin America Transactions, 18(07), 1164 - 1184. https://doi.org/10.1109/TLA.2020.9099757
Hadi, M. U., Al-Tashi, Q., Qureshi, R., Shah, A., Muneer, A., Irfan, M., . . . Shah, M. (10 de Febrero de 2025). Large language models: A comprehensive survey of its applications, challenges, limitations, and future prospects. TechRxiv, 1-54. https://doi.org/10.36227/techrxiv.23589741.v8
Han, W., Xiang, W., Cui, X., Cheng, N., Jiang, G., Qian, W., & Zhang, C. (2025). Prompt engineering 101: Prompt engineering guidelines from a linguistic perspective. En M. Sun, J. Liang, X. Han, Z. Liu, Y. He, G. Rao, . . . Z. Tian (Ed.), Chinese Computational Linguistics. CCL 2024. Lecture Notes in Computer Science (LNAI, volume 14761) (págs. 571 - 592). Singapur: Springer Nature. https://doi.org/10.1007/978-981-97-8367-0_34
Jain, C., Anish, P. R., Singh, A., & Ghaisas, S. (2023). A transformer-based approach for abstractive summarization of requirements from obligations in software engineering contracts. 2023 IEEE 31st International Requirements Engineering Conference (RE), 04-08 September 2023 (págs. 169 - 179). Hannover, Alemania: IEEE. https://doi.org/10.1109/RE57278.2023.00025
Jin, D., Jin, Z., Chen, X., & Wang, C. (2024). ChatModeler: A human-machine collaborative and iterative requirements elicitation and modeling approach via large language models. Journal of Computer Research and Development, 61(2), 338 - 350. https://doi.org/10.7544/issn1000-1239.202330746
Kitchenham, B., & Charters, S. M. (2007). Guidelines for performing systematic literature reviews in software engineering. Version 2.3. EBSE Technical Report EBSE-2007-01, Keele University, y University of Durham. Obtenido de https://legacyfileshare.elsevier.com/promis_misc/525444systematicreviewsguide.pdf
Li, Y. (2023). A practical survey on zero-shot prompt design for In-context learning. En R. Mitkov, & G. Angelova (Ed.), Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing (págs. 641 - 647). Varna, Bulgaria: INCOMA Ltd. Obtenido de https://aclanthology.org/2023.ranlp-1.69/
Lyu, Y., Yan, L., Wang, S., Shi, H., Yin, D., Ren, P., . . . Ren, Z. (2024). KnowTuning: Knowledge-aware fine-tuning for Large Language Models. En Y. Al-Onaizan, M. Bansal, & Y.-N. Chen (Ed.), Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (págs. 14535 - 14556). Miami, Florida, USA: Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.emnlp-main.805
Mendes, E., Wohlin, C., Felizardo, K., & Kalinowski, M. (Septiembre de 2020). When to update systematic literature reviews in software engineering. Journal of Systems and Software, 167, 110607. https://doi.org/10.1016/j.jss.2020.110607
Molla, Y. S., Yimer, S. T., & Alemneh, E. (2023). COSMIC - functional size classification of agile software development: Deep learning approach. En 2023 (Ed.), 2023 International Conference on Information and Communication Technology for Development for Africa (ICT4DA), 26-28 October 2023, Bahir Dar, Etiopia (págs. 155 - 159). IEEE. https://doi.org/10.1109/ICT4DA59526.2023.10302232
Nadeem, A., Sarwar, M. U., & Malik, M. Z. (2021). Automatic issue classifier: A transfer learning framework for classifying issue reports. 2021 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW), 25-28 October 2021, Wuhan, China (págs. 421 - 426). IEEE. https://doi.org/10.1109/ISSREW53611.2021.00113
Nakagawa, H., & Honiden, S. (2023). MAPE-K loop-based goal model generation using generative AI. En K. Schneider, F. Dalpiaz, & J. Horkoff (Ed.), Proceedings - 2023 IEEE 31st International Requirements Engineering Conference Workshops (REW), 04-05 September 2023, Hannover, Alemania (págs. 247 - 251). IEEE. https://doi.org/10.1109/REW57809.2023.00050
Nuseibeh, B., & Easterbrook, S. (2000). Requirements engineering: a roadmap. ICSE '00: Proceedings of the Conference on The Future of Software Engineering (págs. 35 - 46). Limerick, Ireland: Association for Computing Machinery - ACM. https://doi.org/10.1145/336512.336523
OpenAI. (28 de Abril de 2025). OpenAI. Obtenido de sitio web de OpenAI: https://openai.com/
Patil, R., & Gudivada, V. (Marzo de 2024). A review of current trends, techniques, and challenges in Large Language Models (LLMs). Applied Sciences, 14(5). https://doi.org/10.3390/app14052074
Petersen, K., Feldt, R., Mujtaba, S., & Mattsson, M. (2008). Systematic mapping studies in software engineering. 12th International Conference on Evaluation and Assessment in Software Engineering (EASE), 26 - 27 June 2008 (págs. 68 - 77). Bari, Italy: BCS Learning and Development Ltd. https://doi.org/10.14236/ewic/EASE2008.8
Petersen, K., Vakkalanka, S., & Kuzniarz, L. (Agosto de 2015). Guidelines for conducting systematic mapping studies in software engineering: An update. Information and Software Technology, 64, 1 - 18. https://doi.org/10.1016/j.infsof.2015.03.007
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., . . . Liu, P. J. (Enero de 2020). Exploring the limits of transfer learning with a unified text-to-text transformer. (I. Titov, Ed.) Journal of Machine Learning Research, 21(1), 5485 - 5551, Art. No. 140. Obtenido de https://jmlr.org/papers/volume21/20-074/20-074.pdf
Rahimi, N., Eassa, F., & Elrefaei, L. (2020). An ensemble machine learning technique for functional requirement classification. Symmetry, 12(10), 1601. https://doi.org/10.3390/sym12101601
Raiaan, M. A., Mukta, S. H., Fatema, K., Fahad, N. M., Sakib, S., Mim, M. M., . . . Azam, S. (2024). A review on large language models: Architectures, applications, taxonomies, open issues and challenges. IEEE Access, 12, 26839 - 26874. https://doi.org/10.1109/ACCESS.2024.3365742
Rejithkumar, G., Anish, P. R., & Ghaisas, S. (2023). Automated Identification of Deontic Modalities in Software Engineering Contracts: A Domain Adaptation-Based Generative Approach. 2023 IEEE 31st International Requirements Engineering Conference Workshops (REW), 4-5 September 2023, Hannover, Alemania (págs. 72 - 75). Los Alamitos, CA, USA: IEEE Computer Society. https://doi.org/10.1109/REW57809.2023.00020
Restrepo Henao, P., Fischbach, J., Spies, D., Frattini, J., & Vogelsang, A. (2021). Transfer learning for mining feature requests and bug reports from tweets and app store reviews. 2021 IEEE 29th International Requirements Engineering Conference Workshops (REW), Notre Dame, IN, USA (págs. 80 - 86). Los Alamitos, CA, USA: IEEE Computer Society. https://doi.org/10.1109/REW53955.2021.00019
Ronanki, K., Berger, C., & Horkoff, J. (2023). Investigating ChatGPT’s potential to assist in requirements elicitation processes. 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), 06-08 September 2023 (págs. 354 - 361). Durres, Albania: IEEE. https://doi.org/10.1109/SEAA60479.2023.00061
Ruan, K., Chen, X., & Jin, Z. (2023). Requirements modeling aided by ChatGPT: An experience in embedded systems. 2023 IEEE 31st International Requirements Engineering Conference Workshops (REW), 04-05 September 2023, Hannover, Alemania (págs. 170 - 177). IEEE. https://doi.org/10.1109/REW57809.2023.00035
Sainani, A., Anish, P. R., Joshi, V., & Ghaisas, S. (2020). Extracting and classifying requirements from software engineering contracts. 2020 IEEE 28th International Requirements Engineering Conference (RE), 31 August 2020 - 04 September, Zurich, Suiza (págs. 147 - 157). IEEE. https://doi.org/10.1109/RE48521.2020.00026
Sihag, M., Li, Z. S., Dash, A., Arony, N. N., Devathasan, K., Ernst, N., . . . Daniela, D. (2023). A data-driven approach for finding requirements relevant feedback from TikTok and YouTube. 2023 IEEE 31st International Requirements Engineering Conference (RE), 04-08 September 2023, Hannover, Alemania (págs. 111 - 122). IEEE. https://doi.org/10.1109/RE57278.2023.00020
Sommerville, I. (2011). Software engineering (9th ed.). Addison-Wesley. Obtenido de https://engineering.futureuniversity.com/BOOKS%20FOR%20IT/Software-Engineering-9th-Edition-by-Ian-Sommerville.pdf
Stanik, C., Pietz, T., & Maalej, W. (2021). Unsupervised topic discovery in user comments. 2021 IEEE 29th International Requirements Engineering Conference (RE), Notre Dame, IN, USA (págs. 150 - 161). Los Alamitos, CA, USA: IEEE Computer Society. https://doi.org/10.1109/RE51729.2021.00021
Tikayat Ray, A., Cole, B. F., Pinon Fischer, O. J., Bhat, A. P., White, R. T., & Mavris, D. N. (2023). Agile methodology for the standardization of engineering requirements using large language models. Systems, 11(7), 352. https://doi.org/10.3390/systems11070352
Tizard, J., Devine, P., Wang, H., & Blincoe, K. (1 de Abril de 2023). A software requirements ecosystem: linking forum, issue tracker, and FAQs for requirements management. IEEE Transactions on Software Engineering, 49(4), 2381 - 2393. https://doi.org/10.1109/TSE.2022.3219458
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., . . . Polosukhin, I. (2017). Attention is all you need. En I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Ed.), Advances in Neural Information Processing Systems. 31st Conference on Neural Information Processing Systems (NIPS 2017). 30, págs. 1-11. Long Beach, CA, USA: Curran Associates Inc. Obtenido de https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
Wang, Y., Shi, L., Li, M., & Wang, Q. (2020). A deep context-wise method for coreference detection in natural language requirements. 2020 IEEE 28th International Requirements Engineering Conference (RE), 31 August 2020 - 04 September (págs. 180 - 191). Zurich, Suiza: IEEE. https://doi.org/10.1109/RE48521.2020.00029
Wei, J., Courbis, A.-L., Lambolais, T., Xu, B., Bernard, P. L., & Dray, G. (2023). Zero-shot bilingual app reviews mining with large language models. 2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI), Atlanta, GA, USA (págs. 898 - 904). Los Alamitos, CA, USA: IEEE Computer Society. https://doi.org/10.1109/ICTAI59109.2023.00135
Wikipedia. (28 de Enero de 2024). Wikipedia. Obtenido de Sitio web de Wikipedia: https://www.wikipedia.org/
Xia, Y., Zhai, S., Wang, Q., Hou, H., Wu, Z., & Shen, Q. (2022). Automated extraction of ABAC policies from natural-language documents in healthcare systems. 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 06-08 December 2022, Las Vegas, NV, USA (págs. 1289 - 1296). IEEE. https://doi.org/10.1109/BIBM55620.2022.9995559
Zhang, J., Chen, Y., Liu, C., Niu, N., & Wang, Y. (2023). Empirical evaluation of ChatGPT on requirements information retrieval under zero-shot setting. 2023 International Conference on Intelligent Computing and Next Generation Networks(ICNGN), 17-18 November 2023, Hangzhou, China (págs. 1 - 6). IEEE. https://doi.org/10.1109/ICNGN59831.2023.10396810
Zhang, W., Wang, X., Lai, S., Ye, C., & Zhou, H. (2022). Fine-tuning pre-trained model to extract undesired behaviors from app reviews. 2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS), 05-09 December 2022, Guangzhou, China (págs. 1125 - 1134). IEEE. https://doi.org/10.1109/QRS57517.2022.00115
Zhu, R., Li, W., & Jin, C. (2023). TAG: UML activity diagram deeply supervised generation from business textural specification. 2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), 21-24 March 2023, Taipa, Macao (págs. 956 - 961). Los Alamitos, CA, USA: IEEE Computer Society. https://doi.org/10.1109/SANER56733.2023.00116
Descargas
Derechos de autor 2026 Revista Colombiana de Computación

Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.










