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Large Language Models (LLMs) are the cornerstone in automating Requirements Engineering (RE) tasks, underpinning recent advancements in the field.
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: RoBERTa: A robustly optimized BERT pretraining approach (2019). https://doi.org/10.48550/ARXIV.1907.11692
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Sanh, V., Debut, L., Chaumond, J., Wolf, T.: DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter (2019). https://doi.org/10.48550/ARXIV.1910.01108
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Guzman, E., Maalej, W.: How do users like this feature? a fine grained sentiment analysis of app reviews. In: IEEE 22nd International Requirements Engineering Conference (RE). IEEE (2014). https://doi.org/10.1109/re.2014.6912257
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Pennington, J., Socher, R., Manning, C.: GloVe: Global vectors for word representation. In: Conference on Empirical Methods in Natural Language Processing (EMNLP). pp. 1532–1543. Association for Computational Linguistics, Doha, Qatar (2014). https://doi.org/10.3115/v1/D14-1162
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Arora, C., Sabetzadeh, M., Briand, L., Zimmer, F.: Automated extraction and clustering of requirements glossary terms. IEEE Transactions on Software Engineering 43
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Lin, J., Liu, Y., Zeng, Q., Jiang, M., Cleland-Huang, J.: Traceability transformed: Generating more accurate links with pre-trained BERT models. In: IEEE/ACM 43rd International Conference on Software Engineering (ICSE). IEEE (2021). https://doi.org/10.1109/icse43902.2021.00040
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Ezzini, S., Abualhaija, S., Sabetzadeh, M.: WikiDoMiner: Wikipedia domain-specific miner. In: 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE). pp. 1706–1710. Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3540250.3558916
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Fischbach, J., Frattini, J., Vogelsang, A., Mendez, D., Unterkalmsteiner, M., Wehrle, A., Henao, P.R., Yousefi, P., Juricic, T., Radduenz, J., Wiecher, C.: Automatic creation of acceptance tests by extracting conditionals from requirements: NLP approach and case study. Journal of Systems and Software 197
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2016
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30
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Cer, D., Yang, Y., Kong, S.y., Hua, N., Limtiaco, N., John, R.S., Constant, N., Guajardo-Cespedes, M., Yuan, S., Tar, C., Sung, Y.H., Strope, B., Kurzweil, R.: Universal sentence encoder (2018). https://doi.org/10.48550/ARXIV.1803.11175
2018
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Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding (2018). https://doi.org/10.48550/ARXIV.1810.04805
2018
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Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33
2020
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Gülle, K.J., Ford, N., Ebel, P., Brokhausen, F., Vogelsang, A.: Topic modeling on user stories using word mover’s distance. In: IEEE Seventh International Workshop on Artificial Intelligence for Requirements Engineering (AIRE). IEEE (2020). https://doi.org/10.1109/aire51212.2020.00015
2020
Cited alongside, same era.
Hey, T., Keim, J., Koziolek, A., Tichy, W.F.: Norbert: Transfer learning for requirements classification. In: IEEE 28th International Requirements Engineering Conference (RE). IEEE (2020). https://doi.org/10.1109/re48521.2020.00028
2020
Cited alongside, same era.
Chang, Z., Li, M., Wang, Q., Li, S., Wang, J.: Cross-domain requirements linking via adversarial-based domain adaptation. In: IEEE/ACM 45th International Conference on Software Engineering (ICSE). IEEE (2023). https://doi.org/10.1109/icse48619.2023.00138
2023
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Koscinski, V., Hashemi, S., Mirakhorli, M.: On-demand security requirements synthesis with relational generative adversarial networks. In: IEEE/ACM 45th International Conference on Software Engineering (ICSE). IEEE (2023). https://doi.org/10.1109/icse48619.2023.00139
2023
Later among the works it cites.
Luitel, D., Hassani, S., Sabetzadeh, M.: Improving requirements completeness: Automated assistance through large language models. Requirements Engineering Journal (REJ) (2023)
2023
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OpenAI: Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., Almeida, D., et al. : GPT-4 technical report (2023). https://doi.org/10.48550/ARXIV.2303.08774
2023
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Rodriguez, A.D., Dearstyne, K.R., Cleland-Huang, J.: Prompts matter: Insights and strategies for prompt engineering in automated software traceability. In: IEEE 31st International Requirements Engineering Conference Workshops (REW). pp. 455–464 (2023). https://doi.org/10.1109/REW57809.2023.00087
2023
Later among the works it cites.
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., Bikel, D., Blecher, L., Ferrer, C.C., Chen, M., Cucurull, G., Esiobu, D., Fernandes, J., Fu, J., Fu, W., Fuller, B., Gao, C., Goswami, V., Goyal, N., Hartshorn, A., Hosseini, S., Hou, R., Inan, H., Kardas, M., Kerkez, V., Khabsa, M., Kloumann, I., Korenev, A., Koura, P.S., Lachaux, M.A., Lavril, T., Lee, J., Liskovich, D., Lu, Y., Mao, Y., Martinet, X., Mihaylov, T., Mishra, P., Molybog, I., Nie, Y., Poulton, A., Reizenstein, J., Rungta, R., Saladi, K., Schelten, A., Silva, R., Smith, E.M., Subramanian, R., Tan, X.E., Tang, B., Taylor, R., Williams, A., Kuan, J.X., Xu, P., Yan, Z., Zarov, I., Zhang, Y., Fan, A., Kambadur, M., Narang, S., Rodriguez, A., Stojnic, R., Edunov, S., Scialom, T.: Llama 2: Open foundation and fine-tuned chat models (2023). https://doi.org/10.48550/ARXIV.2307.09288
2023
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Guo, J.L.C., Steghöfer, J.P., Vogelsang, A., Cleland-Huang, J.: Natural language processing for requirements traceability. In: Ferrari, A., Deshpande, G. (eds.) Handbook of Natural Language Processing for Requirements Engineering. Springer International Publishing, Cham (2024)
2024
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