Enhanced Structural Validation for UML Use Case Models with Configurable Rule Engine
Year: 2025
Type: conference-paper
Abstract: Automated assessment of use case models in software engineering education requires both rigorous validation and pedagogical adaptability. This paper presents an AI-driven framework specifically designed for use case model evaluation, featuring a four-layer architecture and efficient batch processing via Sparx EA COM integration. The system combines spaCybased syntactic analysis—achieving $92 \%$ accuracy in detecting structural errors unique to use case models—with SBERT-based semantic validation tailored to use case requirements. Dynamic rule configuration is supported through JSON and Excel-based error categorization, enabling flexible adaptation to instructional needs. In a 30 -student pilot(Library Management System)use case models, the framework reduced grading time from 9 hours to 20 minutes and identified 3,050 violations, including 1482 missing pre/post conditions and $\mathbf{1 3 6}$ naming inconsistencies. Customizable lexical rules achieved $92.6 \%$ precision, though $30 \%$ of cases required instructor review due to linguistic ambiguity. Semantic validation provided actionable alerts within 0.4-0.85 confidence thresholds, despite some false positives. Overall, the framework delivers a $27 \times$ speedup and high accuracy in use case model assessment, significantly enhancing UML education. Code and case data are released under CC BY-NC 4.0 for non-commercial research.
Author: Xiangwen Meng
Institution: Shandong University of Science and Technology
Language: English
Subfield: Software
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4411584256