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2023

On Codex Prompt Engineering for OCL Generation: An Empirical Study

Abukhalaf, Seif, Hamdaqa, Mohammad, Khomh, Foutse

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The Object Constraint Language (OCL) is a declarative language that adds constraints and object query expressions to MOF models.

Despite its potential to provide precision and conciseness to UML models, the unfamiliar syntax of OCL has hindered its adoption.
Recent advancements in LLMs, such as GPT-3, have shown their capability in many NLP tasks, including semantic parsing and text generation.
Codex, a GPT-3 descendant, has been fine-tuned on publicly available code from GitHub and can generate code in many programming languages.

On Codex Prompt Engineering for OCL Generation: An Empirical Study · Around

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Similar

I. Bajwa, M. Lee, and B. Bordbar, “Translating natural language constraints to ocl,” Journal of King Saud University - Computer and Information Sciences , vol. 24, p. 117–128, 07 2012
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J. Cabot and M. Gogolla, “Object constraint language (ocl): A definitive guide,” vol. 7320, 06 2012, pp. 58–90
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I. Bajwa, “A natural language processing approach to generate sbvr and ocl,” Ph.D. dissertation, 12 2012
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Object Constraint Language Specifications Version 2.4 . Object Management Group (OMG), 2014
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A. R. Da Silva, “Model-driven engineering: A survey supported by the unified conceptual model,” Computer Languages, Systems & Structures , vol. 43, pp. 139–155, 2015
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T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
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Y. Wang, W. Wang, S. Joty, and S. C. Hoi, “Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation,” arXiv preprint arXiv:2109.00859 , 2021
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M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. d. O. Pinto, J. Kaplan, H. Edwards, Y. Burda, N. Joseph, G. Brockman et al. , “Evaluating large language models trained on code,” arXiv preprint arXiv:2107.03374 , 2021
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P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig, “Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing,” arXiv preprint arXiv:2107.13586 , 2021
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