Ant colony optimization for matching class diagrams

Mojeeb Al Rhman AL-Khiaty*

*Corresponding author for this work

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

1 Scopus citations

Abstract

—Identifying the optimal match between two software models is a preliminary for several model management scenarios. This includes model retrieval, consolidation, and evolution. However, the task has exponential time complexity. Ant Colony Optimization is gaining popularity for providing reasonable solutions for different discrete optimization problems. This paper proposes an Ant Colony algorithm for matching UML class diagrams, with their similarity quantified based on their names, attributes, operations, and structural information. Using a case study of ten pairs of class diagrams, the performance of the Ant Colony Optimization algorithm is empirically tested and compared to that of the basic genetic algorithm, in terms of solution accuracy and execution time. The results indicate the superiority of the Ant Colony algorithm over the genetic algorithm, for the three accuracy measures: accuracy, precision, and recall.

Original languageEnglish
Title of host publication2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2018 - Proceedings
Pages132-135
Number of pages4
ISBN (Electronic)9781538664636
DOIs
StatePublished - 7 Nov 2018

Publication series

NameInternational Scientific and Technical Conference on Computer Sciences and Information Technologies
Volume2
ISSN (Print)2766-3655
ISSN (Electronic)2766-3639

Bibliographical note

Publisher Copyright:
© 2018 IEEE. All rights reserved.

Keywords

  • Ant Colony
  • Class diagram
  • Matching accuracy
  • —model matching

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management

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