Use case diagram similarity measurement: A new approach

Reza Fauzan, Daniel Siahaan, Siti Rochimah, Evi Triandini

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

21 Citations (Scopus)

Abstract

Building a UML diagram from scratch usually requires a lot of resources of software developer. UML reuse is the solution for speeding up software development process. Previous researches utilized diagram similarity measurement for finding reusable diagrams. Many researchers focus on class and sequence diagrams. This research introduces a new approach for measuring use case diagram similarity. This approach combines structural information and lexical information for measuring the similarity of two diagrams. It used to measure structural similarity, i.e. relationship among and between actors and use cases. It also uses Wordnet, WuPalmer, and Cosine Similarity to measure the lexical information semantically, i.e. and text extracted from actors and use cases. The initial result shows that the semantic and structural information could be used as parameters to measure similarity between two use case diagrams.

Original languageEnglish
Title of host publicationProceedings of 2019 International Conference on Information and Communication Technology and Systems, ICTS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3-7
Number of pages5
ISBN (Electronic)9781728121338
DOIs
Publication statusPublished - Jul 2019
Event12th International Conference on Information and Communication Technology and Systems, ICTS 2019 - Surabaya, Indonesia
Duration: 18 Jul 2019 → …

Publication series

NameProceedings of 2019 International Conference on Information and Communication Technology and Systems, ICTS 2019

Conference

Conference12th International Conference on Information and Communication Technology and Systems, ICTS 2019
Country/TerritoryIndonesia
CitySurabaya
Period18/07/19 → …

Keywords

  • Semantic similarity
  • Structural similarity
  • UML similarity
  • Use case similarity

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