  The rapid progress of AI-powered programming assistants, such as GitHub
Copilot, has facilitated the development of software applications. These
assistants rely on large language models (LLMs), which are foundation models
(FMs) that support a wide range of tasks related to understanding and
generating language. LLMs have demonstrated their ability to express UML model
specifications using formal languages like the Object Constraint Language
(OCL). However, the context size of the prompt is limited by the number of
tokens an LLM can process. This limitation becomes significant as the size of
UML class models increases. In this study, we introduce PathOCL, a novel
path-based prompt augmentation technique designed to facilitate OCL generation.
PathOCL addresses the limitations of LLMs, specifically their token processing
limit and the challenges posed by large UML class models. PathOCL is based on
the concept of chunking, which selectively augments the prompts with a subset
of UML classes relevant to the English specification. Our findings demonstrate
that PathOCL, compared to augmenting the complete UML class model
(UML-Augmentation), generates a higher number of valid and correct OCL
constraints using the GPT-4 model. Moreover, the average prompt size crafted
using PathOCL significantly decreases when scaling the size of the UML class
models.
