TY - GEN
T1 - Structural Similarity Measurement using Graph Edit Distance-Greedy on State chart Diagrams
AU - Munawaroh, Hidayatul
AU - Siahaan, Daniel Oranova
AU - Fauzan, Reza
AU - Triandini, Evi
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/27
Y1 - 2020/10/27
N2 - With the emergence of the need for online learning, the automatic grading system is inevitable requirements in an e-learning system. The automatic grading system in software design courses requires a method for measuring similarity between the key-answer design and student-answer designs. There have been several efforts to develop methods for measuring the design similarity. The similarity measurement that has been developed based on semantic or structural aspects of the design. Nevertheless, the purpose of those methods is to reuse software designs. This study proposes a graph representation of the State chart diagram. The graph models the structural aspects of the State chart diagram. This study also proposes the use of Graph Edit Distance (GED) greedy for calculating the structural similarity between two graphs. Graph representation of the State chart diagram is used as input to the GED-greedy method. The results show that all parameters used can determine the structural similarity between two State chart diagrams with graph representation of the State chart diagram as input. State chart similarity results obtained were 0.83.
AB - With the emergence of the need for online learning, the automatic grading system is inevitable requirements in an e-learning system. The automatic grading system in software design courses requires a method for measuring similarity between the key-answer design and student-answer designs. There have been several efforts to develop methods for measuring the design similarity. The similarity measurement that has been developed based on semantic or structural aspects of the design. Nevertheless, the purpose of those methods is to reuse software designs. This study proposes a graph representation of the State chart diagram. The graph models the structural aspects of the State chart diagram. This study also proposes the use of Graph Edit Distance (GED) greedy for calculating the structural similarity between two graphs. Graph representation of the State chart diagram is used as input to the GED-greedy method. The results show that all parameters used can determine the structural similarity between two State chart diagrams with graph representation of the State chart diagram as input. State chart similarity results obtained were 0.83.
KW - Graph Edit Distance (GED)
KW - State chart diagram similarity
KW - graph similarity
KW - greedy
KW - structural similarity
UR - http://www.scopus.com/inward/record.url?scp=85100397924&partnerID=8YFLogxK
U2 - 10.1109/ICORIS50180.2020.9320764
DO - 10.1109/ICORIS50180.2020.9320764
M3 - Conference contribution
AN - SCOPUS:85100397924
T3 - 2020 2nd International Conference on Cybernetics and Intelligent System, ICORIS 2020
BT - 2020 2nd International Conference on Cybernetics and Intelligent System, ICORIS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Cybernetics and Intelligent System, ICORIS 2020
Y2 - 27 October 2020 through 28 October 2020
ER -