A RAG-based Feedback Tool to Augment UML Class Diagram Learning (MODELS 2024 - Tools and Demonstrations)

Sun 22 - Fri 27 September 2024 Linz, Austria

Who

Pasquale Ardimento, Mario Luca Bernardi, Marta Cimitile, Michele Scalera

Track

MODELS 2024 Tools and Demonstrations

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When

Thu 26 Sep 2024 17:09 - 17:27 at HS 1 - MDE and AI (1) Chair(s): Lola Burgueño

Abstract

This paper introduces an advanced functionality designed to facilitate the learning of UML class diagram construction. Built upon an integrated Retrieval Augmented Generation Large Language Model, the functionality provides enriched feedback by leveraging accumulated knowledge. The functionality is implemented in an existing tool named UML Miner, a Visual Paradigm plugin that captures and analyzes student-generated UML diagrams by applying process mining techniques. By offering personalized feedback and continuous support during modeling, the tool aims to enhance learning outcomes and students engagement.

DOI

https://doi.org/10.1145/3652620.3687784

Pasquale Ardimento

Università degli Studi di Bari

Italy

Mario Luca Bernardi

University of Sannio

Italy

Marta Cimitile

Unitelma Sapienza University

Michele Scalera

University of Bari Aldo Moro - Department of Informatics

Italy

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Session Program

Thu 26 Sep
Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

15:45 - 17:30	MDE and AI (1)Technical Track / Tools and Demonstrations at HS 1 Chair(s): Lola Burgueño University of Malaga

15:45 18m Talk		Text2VQL: Teaching a Model Query Language to Open-Source Language Models with ChatGPTFT Technical Track José Antonio Hernández López Linkoping University, Máté Földiák , Daniel Varro Linköping University / McGill University
16:06 18m Talk		Enhancing Automata Learning with Statistical Machine Learning: A Network Security Case StudyPT Technical Track Negin Ayoughi University of Ottawa, Shiva Nejati University of Ottawa, Mehrdad Sabetzadeh University of Ottawa, Patricio Saavedra RabbitRun Technologies Inc Pre-print
16:27 18m Talk		ModelMate: A recommender for textual modeling languages based on pre-trained language modelsFT Technical Track Carlos Durá , José Antonio Hernández López Linkoping University, Jesús Sánchez Cuadrado Universidad de Murcia DOI Authorizer link Pre-print
16:48 18m Talk		DSL-Xpert: LLM-driven Generic DSL Code Generation Tools and Demonstrations Victor Lamas Universidade da Coruña, CITIC, Database Lab, Miguel Rodríguez Luaces Universidade da Coruña, Daniel Garcia-Gonzalez Universidade da Coruña, CITIC, Database Lab
17:09 18m Talk		A RAG-based Feedback Tool to Augment UML Class Diagram Learning Tools and Demonstrations Pasquale Ardimento Università degli Studi di Bari, Mario Luca Bernardi University of Sannio, Marta Cimitile Unitelma Sapienza University, Michele Scalera University of Bari Aldo Moro - Department of Informatics DOI