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Sequence diagram refactoring using single and hybridized algorithms.

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Summary
This summary is machine-generated.

Search-based algorithms effectively refactor sequence diagrams. A hybrid approach combining Kmeans and Simulated Annealing (SA) improves refactoring quality and addresses SA limitations.

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Area of Science:

  • Software Engineering
  • Computer Science

Background:

  • Data mining and search-based algorithms are powerful tools.
  • Previous research explored these algorithms for general refactoring tasks.

Purpose of the Study:

  • To investigate the application of search-based algorithms for sequence diagram refactoring.
  • To evaluate a hybridized Kmeans and Simulated Annealing (SA) algorithm for this task.

Main Methods:

  • Applying search-based algorithms to sequence diagram refactoring.
  • Developing and testing a hybrid Kmeans-SA algorithm.
  • Evaluating results using quality metrics on small and large case studies.

Main Results:

  • Search-based algorithms demonstrate successful sequence diagram refactoring.
  • The hybridized Kmeans-SA algorithm achieved good results based on quality metrics.
  • Hybridization effectively mitigated limitations of the Simulated Annealing algorithm.

Conclusions:

  • Search-based algorithms are viable for sequence diagram refactoring.
  • Hybridizing Kmeans with SA enhances refactoring performance and robustness.