Tipo di tesi
Tesi di laurea magistrale
Titolo
Using machine learning for automatic classification of the layout quality of UML class diagrams
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
ARTIFICIAL INTELLIGENCE AND DATA ENGINEERING
Parole chiave
- artificial intelligence
- computer vision
- data mining
- deep learning
- UML quality AI
- UML schema
- YOLO
Data inizio appello
13/02/2024
Consultabilità
Non consultabile
Data di rilascio
13/02/2094
Riassunto (Italiano)
This thesis focuses on the quality assessment of UML diagram layouts employing cutting-edge machine learning and computer vision techniques. The developed software, following a meticulous training phase, demonstrates the capability to assign a quality grade and furnish constructive feedback to designers upon submitting their schemas. State-of-the-art methodologies were employed, and the achieved results stand as a benchmark in addressing the challenges inherent to UML diagram layout assessment.
The framework leverages advanced tools such as YOLO, PyTorch, OpenCV, and Detecto, including the Faster R-CNN architecture. This amalgamation of frameworks contributes to the robustness and efficiency of the system, ensuring that the outcomes not only meet but also compare favorably with the current state-of-the-art solutions to this pervasive problem.
