Structural and Semantic Similarity Measurement of UML Use Case Diagram
Reusing software has several benefits ranging from reducing cost and risk, accelerating development, and its primary purposes are improving software quality. In the early stage of software development, reusing existing software artifacts may increase the benefit of reusing software because it uses m...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Udayana University, Institute for Research and Community Services
2020-07-01
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| Series: | Lontar Komputer |
| Online Access: | https://ojs.unud.ac.id/index.php/lontar/article/view/59547 |
| _version_ | 1797991440354115584 |
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| author | Mohammad Nazir Arifin Daniel Siahaan |
| author_facet | Mohammad Nazir Arifin Daniel Siahaan |
| author_sort | Mohammad Nazir Arifin |
| collection | DOAJ |
| description | Reusing software has several benefits ranging from reducing cost and risk, accelerating development, and its primary purposes are improving software quality. In the early stage of software development, reusing existing software artifacts may increase the benefit of reusing software because it uses mature artifacts from previous artifacts. One of software artifacts is diagram, and in order to assist the reusing diagram is to find the level of similarity of diagrams. This paper proposes a method for measuring the similarity of the use case diagram using structural and semantic aspects. For structural similarity measurement, Graph Edit Distance is used by transforming each factor and use case into a graph, while for semantic similarity measurement, WordNet, WuPalmer,
and Levenshtein were used. The experimentation was conducted on ten datasets from various
projects. The results of the method were compared with the results of assessments from experts.
The measurement of agreement between experts and method was done by using Gwet’s AC1 and
Pearson correlation coefficient. Measurement results with Gwet’s AC1 diagram similarity are 0,60,
which were categorized as “moderate" agreement and the result of measurement with Pearson
is 0.506 which means there is a significant correlation between experts and methods. The result
showed that the proposed method can be used to find the similarity of the diagram, so finding and
reuse of the diagram as a software component can be optimized. |
| first_indexed | 2024-04-11T08:52:15Z |
| format | Article |
| id | doaj.art-bda61c687e8c4b96a00130ca292c0b44 |
| institution | Directory Open Access Journal |
| issn | 2088-1541 2541-5832 |
| language | English |
| last_indexed | 2024-04-11T08:52:15Z |
| publishDate | 2020-07-01 |
| publisher | Udayana University, Institute for Research and Community Services |
| record_format | Article |
| series | Lontar Komputer |
| spelling | doaj.art-bda61c687e8c4b96a00130ca292c0b442022-12-22T04:33:33ZengUdayana University, Institute for Research and Community ServicesLontar Komputer2088-15412541-58322020-07-011128810010.24843/LKJITI.2020.v11.i02.p0359547Structural and Semantic Similarity Measurement of UML Use Case DiagramMohammad Nazir Arifin0Daniel Siahaan1Universitas MaduraInformatics Department, Institut Sepuluh NopemberReusing software has several benefits ranging from reducing cost and risk, accelerating development, and its primary purposes are improving software quality. In the early stage of software development, reusing existing software artifacts may increase the benefit of reusing software because it uses mature artifacts from previous artifacts. One of software artifacts is diagram, and in order to assist the reusing diagram is to find the level of similarity of diagrams. This paper proposes a method for measuring the similarity of the use case diagram using structural and semantic aspects. For structural similarity measurement, Graph Edit Distance is used by transforming each factor and use case into a graph, while for semantic similarity measurement, WordNet, WuPalmer, and Levenshtein were used. The experimentation was conducted on ten datasets from various projects. The results of the method were compared with the results of assessments from experts. The measurement of agreement between experts and method was done by using Gwet’s AC1 and Pearson correlation coefficient. Measurement results with Gwet’s AC1 diagram similarity are 0,60, which were categorized as “moderate" agreement and the result of measurement with Pearson is 0.506 which means there is a significant correlation between experts and methods. The result showed that the proposed method can be used to find the similarity of the diagram, so finding and reuse of the diagram as a software component can be optimized.https://ojs.unud.ac.id/index.php/lontar/article/view/59547 |
| spellingShingle | Mohammad Nazir Arifin Daniel Siahaan Structural and Semantic Similarity Measurement of UML Use Case Diagram Lontar Komputer |
| title | Structural and Semantic Similarity Measurement of UML Use Case Diagram |
| title_full | Structural and Semantic Similarity Measurement of UML Use Case Diagram |
| title_fullStr | Structural and Semantic Similarity Measurement of UML Use Case Diagram |
| title_full_unstemmed | Structural and Semantic Similarity Measurement of UML Use Case Diagram |
| title_short | Structural and Semantic Similarity Measurement of UML Use Case Diagram |
| title_sort | structural and semantic similarity measurement of uml use case diagram |
| url | https://ojs.unud.ac.id/index.php/lontar/article/view/59547 |
| work_keys_str_mv | AT mohammadnazirarifin structuralandsemanticsimilaritymeasurementofumlusecasediagram AT danielsiahaan structuralandsemanticsimilaritymeasurementofumlusecasediagram |