A case study on using customized large language models for requirement-to-code transformation in software engineering
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Abstract
Large Language Models (LLMs) have shown great promise for assisting software engineering tasks that can be automated, like mapping natural language to code. This paper describes a practical case study using a domain specific LLM to convert software requirements into semi-formal designs, tests and code. We suggest a simple iterative refinement process for engineering prompts and progressively prompting with the intention of improving the accuracy of outputs, alignment to software requirements. We validate our approach with a case study based on real-world scenarios that show how LLMs can be leveraged for requirement extraction, Generation of Object-oriented Design (OOD) and implementation steps. These findings also give an understanding of the utility of LLM-assisted development in relation to affordances and constraints.
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Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. de O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., Ray, A., Puri, R., Krueger, G., Petrov, M., Khlaaf, H., Sastry, G., Mishkin, P., Chan, B., Gray, S., … Zaremba, W. (2021). Evaluating Large Language Models Trained on Code. http://arxiv.org/abs/2107.03374
Dilhara, M., Bellur, A., Bryksin, T., & Dig, D. (2024). Unprecedented Code Change Automation: The Fusion of LLMs and Transformation by Example. Proceedings of the ACM on Software Engineering, 1(FSE), 631–653. https://doi.org/10.1145/3643755
Han, Y., & Lyu, C. (2025). Multi-stage guided code generation for Large Language Models. Engineering Applications of Artificial Intelligence, 139, 109491. https://doi.org/10.1016/J.ENGAPPAI.2024.109491
Li, X.-Y., Xue, J.-T., Xie, Z., & Li, M. (2023). Think Outside the Code: Brainstorming Boosts Large Language Models in Code Generation. http://arxiv.org/abs/2305.10679
Liu, J., Shen, D., Zhang, Y., Dolan, B., Carin, L., & Chen, W. (2022). What Makes Good In-Context Examples for GPT-3? DeeLIO 2022 - Deep Learning Inside Out: 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, Proceedings of the Workshop, 100–114. https://doi.org/10.18653/v1/2022.deelio-1.10
Liu, S., Wen, T., Pattamatta, A. S. L. S., & Srolovitz, D. J. (2024). A prompt-engineered large language model, deep learning workflow for materials classification. Materials Today, 80, 240–249. https://doi.org/10.1016/J.MATTOD.2024.08.028
Pei, Z., Yin, J., & Zhang, J. (2025). Language models for materials discovery and sustainability: Progress, challenges, and opportunities. Progress in Materials Science, 154. https://doi.org/10.1016/j.pmatsci.2025.101495
Pornprasit, C., & Tantithamthavorn, C. (2024). Fine-tuning and prompt engineering for large language models-based code review automation. Information and Software Technology, 175, 107523. https://doi.org/10.1016/J.INFSOF.2024.107523
Qiao, S., Ou, Y., Zhang, N., Chen, X., Yao, Y., Deng, S., Tan, C., Huang, F., & Chen, H. (2023). Reasoning with Language Model Prompting: A Survey. Proceedings of the Annual Meeting of the Association for Computational Linguistics, 1, 5368–5393. https://doi.org/10.18653/v1/2023.acl-long.294
Rubin, O., Herzig, J., & Berant, J. (2022). Learning To Retrieve Prompts for In-Context Learning. NAACL 2022 - 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference, 2655–2671. https://doi.org/10.18653/v1/2022.naacl-main.191
Shi, Z., Wei, J., Xu, Z., & Liang, Y. (2024). Why Larger Language Models Do In-context Learning Differently? Proceedings of Machine Learning Research, 235, 44991–45013.
Sun, Y., Li, X., Liu, C., Deng, X., Zhang, W., Wang, J., Zhang, Z., Wen, T., Song, T., & Ju, D. (2024). Development of an intelligent design and simulation aid system for heat treatment processes based on LLM. Materials and Design, 248. https://doi.org/10.1016/j.matdes.2024.113506
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., & Lample, G. (2023). LLaMA: Open and Efficient Foundation Language Models. http://arxiv.org/abs/2302.13971
White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., & Schmidt, D. C. (2023). A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT. http://arxiv.org/abs/2302.11382
Xie, T., Wan, Y., Huang, W., Zhou, Y., Liu, Y., Linghu, Q., Wang, S., Kit, C., Grazian, C., Zhang, W., & Hoex, B. (2023). Large Language Models as Master Key: Unlocking the Secrets of Materials Science with GPT. http://arxiv.org/abs/2304.02213
Zhao, L., Alhoshan, W., Ferrari, A., Letsholo, K. J., Ajagbe, M. A., Chioasca, E.-V., & Batista-Navarro, R. T. (2021). Natural Language Processing for Requirements Engineering: A Systematic Mapping Study. ACM Comput. Surv., 54(3). https://doi.org/10.1145/3444689