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Finding refactorings that can automatically improve the internal structure of a software system is continuously researched in the search based software engineering literature. In this paper we will investigate through a case study, whether the use of unsupervised learning methods (hierarchical clustering) can be beneficial in the process of automatic refactoring identification. We will compare the results of two algorithms (one that uses hierarchical clustering and one that does not) for a case study, and show that the algorithm that uses hierarchical clustering is capable of finding refactorings which are not found by the other algorithm.

Key words and phrases, software refactoring, hierarchical clustering, software metrics.