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Requirements Engineering (RE) is a critical phase in the software development process that generates requirements specifications from stakeholders' needs.
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A. Ferrari, G. O. Spagnolo, and S. Gnesi, “PURE: A dataset of public requirements documents,” in 25th IEEE International Requirements Engineering Conference , 2017, pp. 502–505
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J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: pre-training of deep bidirectional transformers for language understanding,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics , 2019, pp. 4171–4186
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J. Feng, W. Miao, H. Zheng, Y. Huang, J. Li, Z. Wang, T. Su, B. Gu, G. Pu, M. Yang, and J. He, “FREPA: an automated and formal approach to requirement modeling and analysis in aircraft control domain,” in 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2020, pp. 1376–1386
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L. Shi, M. Xing, M. Li, Y. Wang, S. Li, and Q. Wang, “Detection of hidden feature requests from massive chat messages via deep siamese network,” in 42nd International Conference on Software Engineering , 2020, pp. 641–653
2020
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M. Li, Y. Yang, L. Shi, Q. Wang, J. Hu, X. Peng, W. Liao, and G. Pi, “Automated extraction of requirement entities by leveraging LSTM-CRF and transfer learning,” in IEEE International Conference on Software Maintenance and Evolution , 2020, pp. 208–219
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F. Mu, L. Shi, W. Zhou, Y. Zhang, and H. Zhao, “NERO: A text-based tool for content annotation and detection of smells in feature requests,” in 28th IEEE International Requirements Engineering Conference , 2020, pp. 400–403
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2021
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L. Shi, C. Chen, Q. Wang, and B. W. Boehm, “Automatically detecting feature requests from development emails by leveraging semantic sequence mining,” Requir. Eng. , vol. 26, no. 2, pp. 255–271, 2021. [Online]. Available: https://doi.org/10.1007/s00766-020-00344-y
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S. Ezzini, S. Abualhaija, C. Arora, M. Sabetzadeh, and L. C. Briand, “Using domain-specific corpora for improved handling of ambiguity in requirements,” in 43rd IEEE/ACM International Conference on Software Engineering , 2021, pp. 1485–1497
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C. Wang, L. Hou, and X. Chen, “Extracting requirements models from natural-language document for embedded systems,” in 30th IEEE International Requirements Engineering Conference Workshops , 2022, pp. 18–21
2022
2023
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K. Ruan, X. Chen, and Z. Jin, “Requirements modeling aided by chatgpt: An experience in embedded systems,” in 31st IEEE International Requirements Engineering Conference Workshops , 2023, pp. 170–177
2023
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B. Görer and F. B. Aydemir, “Generating requirements elicitation interview scripts with large language models,” in 31st IEEE International Requirements Engineering Conference, RE 2023 - Workshops, Hannover, Germany, September 4-5, 2023 , 2023, pp. 44–51
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A. Fantechi, S. Gnesi, L. C. Passaro, and L. Semini, “Inconsistency detection in natural language requirements using chatgpt: a preliminary evaluation,” in 31st IEEE International Requirements Engineering Conference Workshop , 2023, pp. 335–340
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Cited alongside, same era.
Q. Zhou, T. Li, and Y. Wang, “Assisting in requirements goal modeling: a hybrid approach based on machine learning and logical reasoning,” in Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems , 2022, pp. 199–209
2022
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S. Ezzini, S. Abualhaija, C. Arora, and M. Sabetzadeh, “Automated handling of anaphoric ambiguity in requirements: A multi-solution study,” in 44th IEEE/ACM 44th International Conference on Software Engineering , 2022, pp. 187–199
2022
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Y. Wang, J. Wang, H. Zhang, X. Ming, L. Shi, and Q. Wang, “Where is your app frustrating users?” in 44th International Conference on Software Engineering , 2022, pp. 2427–2439
2022
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R. Dickler, S. Dudy, A. Mawasi, J. Whitehill, A. Benson, and A. Corbitt, “Interdisciplinary approaches to getting AI experts and education stakeholders talking,” in Artificial Intelligence in Education. Posters and Late Breaking Results , ser. Lecture Notes in Computer Science, vol. 13356, 2022, pp. 115–118
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2023
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D. Jin, C. Wang, and Z. Jin, “Automating extraction of problem diagrams from natural language requirement documents,” in 31st IEEE International Requirements Engineering Conference Workshops , 2023, pp. 199–204
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
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A. D. Rodriguez, K. R. Dearstyne, and J. Cleland-Huang, “Prompts matter: Insights and strategies for prompt engineering in automated software traceability,” in 31st IEEE International Requirements Engineering Conference Workshops , 2023, pp. 455–464
2023
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2023
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2023
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2023
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2023
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W. Alhoshan, A. Ferrari, and L. Zhao, “Zero-shot learning for requirements classification: An exploratory study,” Inf. Softw. Technol. , vol. 159, p. 107202, 2023
2023
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