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Design smell detection has proven to be a significant activity that has an aim of not only enhancing the software quality but also increasing its life cycle.

This work investigates whether machine learning approaches can effectively be leveraged for software design smell detection. Additionally, this paper provides a comparatively study, focused on using balanced datasets, where it checks if avoiding dataset balancing can be of any influence on the accuracy and behavior during design smell detection.

A set of experiments have been conducted-using 28 Machine Learning classifiers aimed at detecting God classes. This experiment was conducted using a dataset formed from 12,587 classes of 24 software systems, in which 1,958 classes were manually validated.

Ultimately, most classifiers obtained high performances,-with Cat Boost showing a higher performance. Also, it is evident from …