Lola Burgueño

Meriem Ben Chaaben, Lola Burgueño, Houari Sahraoui

We propose a simple yet a novel approach to improve completion in domain modeling activities. Our approach exploits the power of large language models by using few-shot prompt learning without the need to train or fine-tune those models with large datasets that are scarce in this field. We implemented our approach and tested it on the completion of static and dynamic domain diagrams. Our initial evaluation shows that such an approach is effective and can be integrated in different ways during the modeling activities.

Andrei Coman, Lola Burgueño, Dominik Bork et al.

Large Language Models (LLMs) have been recently proposed for supporting domain modeling tasks mostly related to the completion of partial models by recommending additional model elements. However, there are many more modeling tasks, one of them being the instantiation of domain models to represent concrete domain objects. While there is considerable work supporting the generation of structurally valid instantiations, there are still open challenges to incorporating real-world semantics by having realistic values contained in instances and ensuring the generation of semantically diverse models. Only then will such generated models become human-understandable and helpful in educational or data-driven research contexts. To tackle these challenges, this paper presents an approach that employs LLMs and two prompting strategies in combination with existing model validation tools for instantiating semantically realistic and diverse domain models expressed as UML class diagrams. We have applied our approach to models used in education and available in the literature from different domains and evaluated the generated instances in terms of syntactic correctness, model conformance, semantic correctness, and diversity of the generated values. The results show that the generated instances are mostly syntactically correct, that they conform to the domain model, and that there are only a few semantic errors. Moreover, the generated instance values are semantically diverse, i.e., concrete realistic examples in line with the domain and the combination of the values within one model are semantically coherent.

Shwetali Shimangaud, Lola Burgueño, Rijul Saini et al.

Context: Having domain models derived from textual specifications has proven to be very useful in the early phases of software engineering. However, creating correct domain models and establishing clear links with the textual specification is a challenging task, especially for novice modelers. Objectives: We propose an approach for determining the alignment between a partial domain model and a textual specification. Methods: To this aim, we use Natural Language Processing techniques to pre-process the text, generate an artificial natural language specification for each model element, and then use an LLM to compare the generated description with matched sentences from the original specification. Ultimately, our algorithm classifies each model element as either aligned (i.e., correct), misaligned (i.e., incorrect), or unclassified (i.e., insufficient evidence). Furthermore, it outputs the related sentences from the textual specification that provide the evidence for the determined class. Results: We have evaluated our approach on a set of examples from the literature containing diverse domains, each consisting of a textual specification and a reference domain model, as well as on models containing modeling errors that were systematically derived from the correct models through mutation. Our results show that we are able to identify alignments and misalignments with a precision close to 1 and a recall of approximately 78%, with execution times ranging from 18 seconds to 1 minute per model element. Conclusion: Since our algorithm almost never classifies model elements incorrectly, and is able to classify over 3/4 of the model elements, it could be integrated into a modeling tool to provide positive feedback or generate warnings, or employed for offline validation and quality assessment.

Meriem Ben Chaaben, Lola Burgueño, Istvan David et al.

Model-driven engineering (MDE) simplifies software development through abstraction, yet challenges such as time constraints, incomplete domain understanding, and adherence to syntactic constraints hinder the design process. This paper presents a study to evaluate the usefulness of a novel approach utilizing large language models (LLMs) and few-shot prompt learning to assist in domain modeling. The aim of this approach is to overcome the need for extensive training of AI-based completion models on scarce domain-specific datasets and to offer versatile support for various modeling activities, providing valuable recommendations to software modelers. To support this approach, we developed MAGDA, a user-friendly tool, through which we conduct a user study and assess the real-world applicability of our approach in the context of domain modeling, offering valuable insights into its usability and effectiveness.