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.

In this paper, we present a novel approach NL2OCL to translate English specification of constraints to formal constraints such as OCL (Object Constraint language). In the used approach, input English constraints are syntactically and semantically analyzed to generate a SBVR (Semantics of Business Vocabulary and Rules) based logical representation that is finally mapped to OCL. During the syntactic and semantic analysis we have also addressed various syntactic and semantic ambiguities that make the presented approach robust. The presented approach is implemented in Java as a proof of concept. A case study has also been solved by using our tool to evaluate the accuracy of the presented approach. The results of evaluation are also compared to the pattern based approach to highlight the significance of the used approach.