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[Context]: Companies are increasingly recognizing the importance of automating Requirements Engineering (RE) tasks due to their resource-intensive nature.
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Berry, D.M., Cleland-Huang, J., Ferrari, A., Maalej, W., Mylopoulos, J., Zowghi, D.: Panel: Context-Dependent Evaluation of Tools for NL RE Tasks: Recall vs. Precision, and Beyond. In: 2017 IEEE 25th International Requirements Engineering Conference (RE). pp. 570–573 (2017). https://doi.org/10.1109/RE.2017.64
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Ferrari, A., Spagnolo, G.O., Gnesi, S.: PURE: A Dataset of Public Requirements Documents. In: 2017 IEEE 25th International Requirements Engineering Conference (RE). pp. 502–505 (2017). https://doi.org/10.1109/RE.2017.29
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Reynolds, Laria and McDonell, Kyle: Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm. In: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems. CHI EA ’21, Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3411763.3451760, https://doi.org/10.1145/3411763.3451760
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2022
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Liu, V., Chilton, L.B.: Design Guidelines for Prompt Engineering Text-to-Image Generative Models. In: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. CHI ’22, Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3491102.3501825, https://doi.org/10.1145/3491102.3501825
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2023
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Fiannaca, Alexander J. and Kulkarni, Chinmay and Cai, Carrie J and Terry, Michael: Programming without a Programming Language: Challenges and Opportunities for Designing Developer Tools for Prompt Programming. In: Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems. CHI EA ’23, Association for Computing Machinery, New York, NY, USA (2023). https://doi.org/10.1145/3544549.3585737, https://doi.org/10.1145/3544549.3585737
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2023
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White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT (2023)
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Nguyen, N., Nadi, S.: An Empirical Evaluation of GitHub Copilot’s Code Suggestions. In: Proceedings of the 19th International Conference on Mining Software Repositories. p. 1–5. MSR ’22, Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3524842.3528470, https://doi.org/10.1145/3524842.3528470
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Wei, J., Wang, X., Schuurmans, D., Bosma, M., ichter, b., Xia, F., Chi, E., Le, Q.V., Zhou, D.: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds.) Advances in Neural Information Processing Systems. vol. 35, pp. 24824–24837. Curran Associates, Inc. (2022), https://proceedings.neurips.cc/paper_files/paper/2022/file/9d5609613524ecf4f15af0f7b31abca4-Paper-Conference.pdf
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Alhoshan, W., Ferrari, A., Zhao, L.: Zero-shot Learning for Requirements Classification: An Exploratory Study. Information and Software Technology 159
2023
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Li, X., Wang, B., Wan, H., Deng, Y., Wang, Z.: Applications of Machine Learning in Requirements Traceability: A Systematic Mapping Study
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2023
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White, J., Hays, S., Fu, Q., Spencer-Smith, J., Schmidt, D.C.: ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design (2023)
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Zhang, J., Chen, Y., Niu, N., Wang, Y., Liu, C.: Empirical Evaluation of ChatGPT on Requirements Information Retrieval Under Zero-Shot Setting (2023)
2023
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