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The code smell is a sign of design and development flaws in a software systemthat reduces the reusability and maintainability of the system. Refactoring isdone as an ongoing practice to remove the code smell from the program code.Among different code smells, the God class or Blob is one of the most commoncode smells. A god class contains too many responsibilities, violatingobject-oriented programming design's low coupling and high cohesivenessprinciples. This paper proposes an automatic approach to extracting a God classinto multiple smaller classes with more specific responsibilities. To do this,we first construct a graph of methods (as nodes) for the concerning god class.The edge between any two methods is determined by their structural similarity,and the feature for each method is initialized using different semanticrepresentation methods. Then, the variational graph auto-encoder is used tolearn a vector representation for each method. Finally, the learned vectors areused to cluster methods into different groups to be recommended as refactoredclasses. We assessed the proposed framework using three different classcohesion metrics on sixteen actual God Classes collected from two well-knownopen-source systems. We also conducted a comparative study of our approach witha similar existing approach and found that the proposed approach generatedbetter results for almost all the God Classes used in the experiment.

Exploring Variational Graph Auto-Encoders for Extract Class Refactoring Recommendation