Proposal and Evaluation of a Method to Semi-Automatically Identify and Measure Unmet Requirements in Learner-Created State Machine Diagrams

Abstract

The UML state machine diagram notation is complex and challenging for learners to understand. Hence, educators assign modeling assignments to learners and provide feedback on “unmet requirements and their causes” inferred from errors in their diagrams. However, learner-created diagrams vary in content and layout, making it difficult for educators to quickly correctly identify unmet requirements. Therefore, this paper proposes a method to semi-automatically identify and measure unmet requirements in learner-created diagrams based on a solution example diagram. The method focuses on modeling assignments that involve selecting expressions for do-activities and triggers from a glossary, dealing with variable processing in guards and effects. It associates requirements with “cases” which represent behaviors that satisfy the requirements by connecting partial diagrams of the solution example. It then identifies unmet requirements and cases in the learner-created diagrams using model checking. Applying the proposed method to 38 learner-created diagrams validly identified “unmet requirements” in all of them.