AAUC: A Feedback‐Centric Framework for Automated Assessment of UML Use‐Case Diagrams

Abstract

Assessing diagrammatic artifacts such as UML use‐case diagrams is a core yet challenging task in engineering and software engineering education. In large classes, manually evaluating such diagrams is time‐consuming, subjective, and difficult to scale, particularly when student solutions exhibit structural variations and diverse labeling choices. These challenges limit the consistency of grading and the timeliness of feedback provided to students. This article presents Automated Assessment of Use–Case Diagrams (AAUC), a feedback‐centric framework and tool for the automated assessment of UML use‐case diagrams, designed to support assessment practices in engineering education. The proposed approach integrates label and structure matching, along with instructor‐configurable marking rules, within a level‐aware assessment model that supports partial credit and differentiated evaluation. Unlike approaches that focus solely on correctness detection, AAUC emphasizes the generation of meaningful formative feedback by identifying specific modeling issues, such as missing actors, incorrect relationships, and inconsistent or incomplete labeling. The framework has been evaluated using a dataset of 445 student‐generated use‐case diagrams collected over multiple academic years from undergraduate software engineering and systems analysis courses. The evaluation demonstrates that the system can robustly assess diverse student submissions, apply consistent marking policies, and generate both quantitative scores and qualitative feedback aligned with instructor expectations. By combining automated assessment with explicit feedback generation, AAUC addresses scalability challenges while supporting iterative improvement in student modeling work. The framework provides a practical, extensible solution for instructors seeking to integrate automated support into engineering education contexts involving diagrammatic design tasks.