{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T17:05:01Z","timestamp":1785431101614,"version":"3.56.0"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032227737","type":"print"},{"value":"9783032227744","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-22774-4_3","type":"book-chapter","created":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T09:18:04Z","timestamp":1776331084000},"page":"43-63","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From Words to Code: Do NLP Prompting Strategies Generalize to Code Generation?"],"prefix":"10.1007","author":[{"given":"Erin","family":"Woo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sangyeop","family":"Yeo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyungkook","family":"Jun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sangcheol","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seung-won","family":"Hwang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Seung","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,17]]},"reference":[{"key":"3_CR1","unstructured":"LiveCodeBench Leaderboard (2025), https:\/\/livecodebench.github.io\/leaderboard.html, accessed: 2025-04-24"},{"key":"3_CR2","unstructured":"Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et\u00a0al.: Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)"},{"key":"3_CR3","unstructured":"Bercovich, A., Levy, I., Golan, I., Dabbah, M., El-Yaniv, R., Puny, O., Galil, I., Moshe, Z., Ronen, T., Nabwani, N., et\u00a0al.: Llama-nemotron: Efficient reasoning models. arXiv preprint arXiv:2505.00949 (2025)"},{"key":"3_CR4","unstructured":"Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: Language models are few-shot learners. In: Advances in Neural Information Processing Systems (NeurIPS) 2020 (2020)"},{"key":"3_CR5","unstructured":"Bsharat, S.M., Myrzakhan, A., Shen, Z.: Principled instructions are all you need for questioning llama-1\/2, gpt-3.5\/4. arXiv preprint arXiv:2312.16171 (2023), code available at https:\/\/github.com\/VILA-Lab\/ATLAS"},{"key":"3_CR6","doi-asserted-by":"crossref","unstructured":"Casalnuovo, C., Sagae, K., Devanbu, P.: Studying the difference between natural and programming language corpora. Empirical Software Engineering 24(4), 1823\u20131868 (2019)","DOI":"10.1007\/s10664-018-9669-7"},{"key":"3_CR7","unstructured":"Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.D.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et\u00a0al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021)"},{"key":"3_CR8","unstructured":"DeepMind, G.: Gemini 2.5: Our most intelligent ai model. Tech. rep. (2025), https:\/\/blog.google\/technology\/google-deepmind\/gemini-model-thinking-updates-march-2025\/"},{"key":"3_CR9","doi-asserted-by":"publisher","unstructured":"Ding, H., Fan, Z., Guehring, I., Gupta, G., Ha, W., Huan, J., Liu, L., Omidvar-Tehrani, B., Wang, S., Zhou, H.: Reasoning and planning with large language models in code development. In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. p. 6480\u20136490. KDD \u201924, Association for Computing Machinery, New York, NY, USA (2024). https:\/\/doi.org\/10.1145\/3637528.3671452, https:\/\/doi.org\/10.1145\/3637528.3671452","DOI":"10.1145\/3637528.3671452"},{"key":"3_CR10","unstructured":"Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Yang, A., Fan, A., et\u00a0al.: The llama 3 herd of models. arXiv preprint arXiv:2407.21783 (2024)"},{"key":"3_CR11","doi-asserted-by":"publisher","unstructured":"Fan, L., Chen, M., Liu, Z.: SEK: Self-explained keywords empower large language models for code generation. In: Che, W., Nabende, J., Shutova, E., Pilehvar, M.T. (eds.) Findings of the Association for Computational Linguistics: ACL 2025. pp. 6249\u20136278. Association for Computational Linguistics, Vienna, Austria (Jul 2025). https:\/\/doi.org\/10.18653\/v1\/2025.findings-acl.324, https:\/\/aclanthology.org\/2025.findings-acl.324\/","DOI":"10.18653\/v1\/2025.findings-acl.324"},{"key":"3_CR12","unstructured":"Guo, D., Yang, D., Zhang, H., Song, J., Zhang, R., Xu, R., Zhu, Q., Ma, S., Wang, P., Bi, X., et\u00a0al.: Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948 (2025)"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Han, H., Kim, J., Yoo, J., Lee, Y., Hwang, S.w.: Archcode: Incorporating software requirements in code generation with large language models. In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 13520\u201313552 (2024)","DOI":"10.18653\/v1\/2024.acl-long.730"},{"key":"3_CR14","unstructured":"Holtzman, A., Buys, J., Du, L., Forbes, M., Choi, Y.: The curious case of neural text degeneration. arXiv preprint arXiv:1904.09751 (2019)"},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Hou, X., Zhao, Y., Liu, Y., Yang, Z., Wang, K., Li, L., Luo, X., Lo, D., Grundy, J., Wang, H.: Large language models for software engineering: A systematic literature review 33(8) (2024)","DOI":"10.1145\/3695988"},{"key":"3_CR16","unstructured":"Huang, D., Bu, Q., Qing, Y., Cui, H.: Codecot: Tackling code syntax errors in cot reasoning for code generation. arXiv preprint arXiv:2308.08784 (2023)"},{"key":"3_CR17","unstructured":"Hui, B., Yang, J., Cui, Z., Yang, J., Liu, D., Zhang, L., Liu, T., Zhang, J., Yu, B., Lu, K., et\u00a0al.: Qwen2.5-coder technical report. arXiv preprint arXiv:2409.12186 (2024)"},{"key":"3_CR18","unstructured":"Jain, N., Han, K., Gu, A., Li, W.D., Yan, F., Zhang, T., Wang, S., Solar-Lezama, A., Sen, K., Stoica, I.: Livecodebench: Holistic and contamination free evaluation of large language models for code. arXiv preprint arXiv:2403.07974 (2024)"},{"key":"3_CR19","doi-asserted-by":"publisher","unstructured":"Jha, S.K., Jha, S., Ewetz, R., Velasquez, A.: Co-synthesis of code and formal models using large language models and functors. In: MILCOM 2024 - 2024 IEEE Military Communications Conference (MILCOM). pp. 215\u2013220 (2024). https:\/\/doi.org\/10.1109\/MILCOM61039.2024.10773930","DOI":"10.1109\/MILCOM61039.2024.10773930"},{"key":"3_CR20","doi-asserted-by":"publisher","unstructured":"Jiang, X., Dong, Y., Wang, L., Fang, Z., Shang, Q., Li, G., Jin, Z., Jiao, W.: Self-planning code generation with large language models. ACM Transactions on Software Engineering and Methodology 33(7) (Sep 2024). https:\/\/doi.org\/10.1145\/3672456, https:\/\/doi.org\/10.1145\/3672456","DOI":"10.1145\/3672456"},{"key":"3_CR21","unstructured":"Kong, A., Zhao, S., Chen, H., Li, Q., Qin, Y., Sun, R., Zhou, X., Wang, E., Dong, X.: Better zero-shot reasoning with role-play prompting. arXiv preprint arXiv:2308.07702 (2023)"},{"key":"3_CR22","doi-asserted-by":"publisher","unstructured":"Li, J., Li, G., Li, Y., Jin, Z.: Structured chain-of-thought prompting for code generation. ACM Transactions on Software Engineering and Methodology 34(2) (Jan 2025). https:\/\/doi.org\/10.1145\/3690635, https:\/\/doi.org\/10.1145\/3690635","DOI":"10.1145\/3690635"},{"key":"3_CR23","doi-asserted-by":"publisher","unstructured":"Li, J., Zhao, Y., Li, Y., Li, G., Jin, Z.: Acecoder: An effective prompting technique specialized in code generation. ACM Trans. Softw. Eng. Methodol. 33(8) (Nov 2024). https:\/\/doi.org\/10.1145\/3675395, https:\/\/doi.org\/10.1145\/3675395","DOI":"10.1145\/3675395"},{"key":"3_CR24","unstructured":"Li, X., Zhao, R., Chia, Y.K., Ding, B., Joty, S., Poria, S., Bing, L.: Chain-of-knowledge: Grounding large language models via dynamic knowledge adapting over heterogeneous sources. arXiv preprint arXiv:2305.13269 (2023)"},{"key":"3_CR25","unstructured":"OpenAI: Openai o1 system card. Tech. rep., OpenAI (2024), https:\/\/openai.com\/index\/openai-o1-system-card\/"},{"key":"3_CR26","doi-asserted-by":"publisher","unstructured":"Peng, Q., Zhang, C., Mangal, R., Pasareanu, C., Jia, L.: Random Perturbation Attack on LLMs for Code Generation . In: 2025 IEEE\/ACM 4th International Conference on AI Engineering \u2013 Software Engineering for AI (CAIN). pp. 285\u2013287. IEEE Computer Society, Los Alamitos, CA, USA (Apr 2025). https:\/\/doi.org\/10.1109\/CAIN66642.2025.00052, https:\/\/doi.ieeecomputersociety.org\/10.1109\/CAIN66642.2025.00052","DOI":"10.1109\/CAIN66642.2025.00052"},{"key":"3_CR27","doi-asserted-by":"publisher","unstructured":"Qiao, S., Ou, Y., Zhang, N., Chen, X., Yao, Y., Deng, S., Tan, C., Huang, F., Chen, H.: Reasoning with language model prompting: A survey. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 5368\u20135393. Association for Computational Linguistics, Toronto, Canada (Jul 2023). https:\/\/doi.org\/10.18653\/v1\/2023.acl-long.294, https:\/\/aclanthology.org\/2023.acl-long.294\/","DOI":"10.18653\/v1\/2023.acl-long.294"},{"key":"3_CR28","unstructured":"Sahoo, P., Singh, A.K., Saha, S., Jain, V., Mondal, S., Chadha, A.: A systematic survey of prompt engineering in large language models: Techniques and applications (2025), https:\/\/arxiv.org\/abs\/2402.07927"},{"key":"3_CR29","unstructured":"Wang, X., Wei, J., Schuurmans, D., Le, Q.V., Chi, E.H., Narang, S., Chowdhery, A., Zhou, D.: Self-consistency improves chain of thought reasoning in language models. In: The Eleventh International Conference on Learning Representations (2023), https:\/\/openreview.net\/forum?id=1PL1NIMMrw"},{"key":"3_CR30","doi-asserted-by":"crossref","unstructured":"Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q.V., Zhou, D., et\u00a0al.: Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems 35, 24824\u201324837 (2022)","DOI":"10.52202\/068431-1800"},{"key":"3_CR31","unstructured":"Xu, B., Yang, A., Lin, J., Wang, Q., Zhou, C., Zhang, Y., Mao, Z.: Expertprompting: Instructing large language models to be distinguished experts. arXiv preprint arXiv:2305.14688 (2023)"},{"key":"3_CR32","doi-asserted-by":"crossref","unstructured":"Yang, G., Zhou, Y., Chen, X., Zhang, X., Zhuo, T.Y., Chen, T.: Chain-of-thought in neural code generation: From and for lightweight language models. IEEE Transactions on Software Engineering (2024)","DOI":"10.1109\/TSE.2024.3440503"},{"key":"3_CR33","doi-asserted-by":"crossref","unstructured":"Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T., Cao, Y., Narasimhan, K.: Tree of thoughts: Deliberate problem solving with large language models. Advances in Neural Information Processing Systems 36 (2024)","DOI":"10.52202\/075280-0517"},{"key":"3_CR34","unstructured":"Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: React: Synergizing reasoning and acting in language models. In: International Conference on Learning Representations (ICLR) (2023)"},{"key":"3_CR35","unstructured":"Yeo, S., Hwang, S.W., Ma, Y.S.: Chain of Grounded Objectives: Concise goal-oriented prompting for code generation. In: 39th European Conference on Object-Oriented Programming (ECOOP 2025). pp. 35\u20131. Schloss Dagstuhl\u2013Leibniz-Zentrum f\u00fcr Informatik (2025)"},{"key":"3_CR36","doi-asserted-by":"publisher","unstructured":"Zheng, Z., Ning, K., Zhong, Q., Chen, J., Chen, W., Guo, L., Wang, W., Wang, Y.: Towards an understanding of large language models in software engineering tasks. Empirical Software Engineering 30(2) (Dec 2024). https:\/\/doi.org\/10.1007\/s10664-024-10602-0, https:\/\/doi.org\/10.1007\/s10664-024-10602-0","DOI":"10.1007\/s10664-024-10602-0"}],"container-title":["Lecture Notes in Computer Science","Fundamental Approaches to Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-22774-4_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T16:43:19Z","timestamp":1785429799000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-22774-4_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032227737","9783032227744"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-22774-4_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"17 April 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FASE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Foundations and Formal Methods for Software and Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Turin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 April 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 April 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"fase2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/etaps.org\/2026\/conferences\/fase\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}