Meriem Ben Chaaben

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.

Yogya Gamage, Meriem Ben Chaaben, Martin Monperrus et al.

Automated third-party library analysis tools help developers by addressing key dependency management challenges, such as automating version updates, detecting vulnerabilities, and detecting breaking updates. Dependency reachability analysis aims at improving the precision of dependency management, by reducing the space of dependency issues to the ones that actually matter. Most tools for dependency reachability analysis are static and fundamentally limited by the absence of execution. In this paper, we propose FIKA, a pipeline for providing guarantees of executability for third-party library call sites. FIKA generates code that is executed, and whose execution trace provides guarantees that a third-party library call site is actually reachable. We apply our approach to a dataset of eight Java projects to empirically evaluate the effectiveness of FIKA. On average, 54% of these call sites are covered by the existing test suites, and therefore, have evidence for their executability. FIKA further improves this coverage by 20% and is able to demonstrate executability for 2363 dependency methods. In six out of eight projects, FIKA provides strong guarantees that more than 75% of call sites are executable. We further demonstrate that FIKA is capable of improving the results provided by Semgrep, a state-of-the-art static vulnerability reachability analysis tool. We show that FIKA can help prioritize the vulnerability updates with stronger guarantees of executability in cases where Semgrep yields inconclusive reachability results.

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.