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Research Article | Open Access |

Enhancement of Design Level Class Decomposition using Evaluation Process

Author 1: Bayu Priyambadha Author 2: Tetsuro Katayama
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 13, No. 8 · Published 2022

DOI: https://doi.org/10.14569/IJACSA.2022.0130816

Abstract

Refactoring on the design level artifact such as the class diagram was already done using the threshold-based agglomerative hierarchical clustering method, specifically class decomposition. The approach produced a better cluster based on the label name similarity of attribute and method. But, some problems emerge from the experiment result. The negative Silhouettes element still exist in the cluster. And, there is an unusable cluster that only consists of one attribute element. This paper has proposed the evaluation process to optimize the result of clustering. This evaluation process is an additional process that aims to move the negative Silhouettes element to the other cluster. The movement is also to get the better value of element Silhouettes value. The evaluation process can produce a better result for clusters. The clusters produced from the evaluation process have higher Silhouettes values. The average Silhouettes value is increased by about 40%. Ultimately, the result shows no unusable cluster as mentioned in the previous research.

Keywords

How to Cite this Article

Priyambadha, B., & Katayama, T. (2022). Enhancement of Design Level Class Decomposition using Evaluation Process. International Journal of Advanced Computer Science and Applications, 13(8). https://doi.org/10.14569/IJACSA.2022.0130816

Priyambadha, Bayu, and Tetsuro Katayama. "Enhancement of Design Level Class Decomposition using Evaluation Process." International Journal of Advanced Computer Science and Applications, vol. 13, no. 8, 2022, https://doi.org/10.14569/IJACSA.2022.0130816.

@article{Priyambadha2022,
  title     = {Enhancement of Design Level Class Decomposition using Evaluation Process},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {8},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Bayu Priyambadha and Tetsuro Katayama},
  doi       = {10.14569/IJACSA.2022.0130816},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130816}
}

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