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Research Article | Open Access |

Design Level Class Decomposition using the Threshold-based Hierarchical Agglomerative Clustering

Author 1: Bayu Priyambadha Author 2: Tetsuro Katayama
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 13, No. 3 · Published 2022

DOI: https://doi.org/10.14569/IJACSA.2022.0130310

Abstract

Refactoring activity is essential to maintain the quality of a software’s internal structure. It decays as the impact of software changes and evolution. Class decomposition is one of the refactoring processes in maintaining internal quality. Mostly, the refactoring process is done at the level of source code. Shifting from source code level to design level is necessary as a quick step to refactoring and close to the requirement. The design artifact has a higher abstraction level than the source code and has limited information. The challenge is to define new metrics needed in class decomposition using the design artifact's information. Syntactic and semantic information from the design artifact provides valuable data for the decomposition process. Class decomposition can be done at the level of design artifact (class diagram) using syntactic and semantic information. The dynamic threshold-based Hierarchical Agglomerative Clustering produces a more specific cluster that is considered to produce a single responsibility class.

Keywords

How to Cite this Article

Priyambadha, B., & Katayama, T. (2022). Design Level Class Decomposition using the Threshold-based Hierarchical Agglomerative Clustering. International Journal of Advanced Computer Science and Applications, 13(3). https://doi.org/10.14569/IJACSA.2022.0130310

Priyambadha, Bayu, and Tetsuro Katayama. "Design Level Class Decomposition using the Threshold-based Hierarchical Agglomerative Clustering." International Journal of Advanced Computer Science and Applications, vol. 13, no. 3, 2022, https://doi.org/10.14569/IJACSA.2022.0130310.

@article{Priyambadha2022,
  title     = {Design Level Class Decomposition using the Threshold-based Hierarchical Agglomerative Clustering},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {3},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Bayu Priyambadha and Tetsuro Katayama},
  doi       = {10.14569/IJACSA.2022.0130310},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130310}
}

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