Proposal and Evaluation of an Automated Method of Identifying Errors in Learner's UML State Machine Diagrams by Combination of Static and Dynamic Analysis

Abstract

In UML state machine diagram education, it is necessary for educators to identify the location and type of errors in each answer and explain them to students in order to promote correct understanding among students. In this study, we propose an automated method to identify errors in UML state machine diagrams by static and dynamic analysis so that educators can efficiently and correctly identify errors in the UML state machine diagrams that serve as the assignment answers. In an evaluation experiment, we applied the proposed method to 95 answer sheets prepared by 19 information science students in five different tasks. The results showed that the proposed method did not incorrectly identify errors in answers that did not contain errors, and that it properly identified at least one error in each answer that contained an error. The educators evaluated that the proposed method was useful in terms of accuracy of error identification, although it did not completely identify errors that should be explained.