UML Miner leverages a Retrieval-Augmented Generation (RAG) large language model to provide contextualized feedback on UML class diagrams. By combining the learner's modeling context with instructional resources, it helps students identify issues, improve their models, and refine their design decisions. Feedback is available through predefined or free-form queries using either a standalone LLM or a RAG configuration.
It exports all the Logs generated in the XES format. The XES standard defines a grammar for a tag-based language whose aim is to provide designers of information systems with a unified and extensible methodology for capturing systems behaviors by means of event logs and event streams is defined in the XES standard. An example of event log is available here.
It finds out the actual modelling process which is happening inside a software project. To this aim, it allows to execute the Declare Miner and MINERful techniques to process discovery.
It figures out if there are any deviations between the actual modelling process and the ideal modelling process (from the model). To this aim, it allows to execute the Declare Analyzer and the Declare Replayer methods to conformance checking. The conformance can be explored either by trace or by constraint.
It serves as a powerful tool for refining searches within log files containing student modeling actions, enabling users to extract valuable insights and support effective instructional practices in UML diagram development.
It provides a capability to generate human-readable descriptions of UML diagrams and models. Providing the description in natural language of the solution given by the teacher enriches LLM-RAG’s ability to offer detailed and contextually relevant feedback.