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To reduce technical debt and make code more maintainable, it is important tobe able to warn programmers about code smells. State-of-the-art code smalldetectors use deep learners, without much exploration of alternatives withinthat technology.  One promising alternative for software analytics and deep learning is GHOST(from TSE'21) that relies on a combination of hyper-parameter optimization offeedforward neural networks and a novel oversampling technique to deal withclass imbalance.  The prior study from TSE'21 proposing this novel "fuzzy sampling" wassomewhat limited in that the method was tested on defect prediction, butnothing else. Like defect prediction, code smell detection datasets have aclass imbalance (which motivated "fuzzy sampling"). Hence, in this work we testif fuzzy sampling is useful for code smell detection.  The results of this paper show that we can achieve better thanstate-of-the-art results on code smell detection with fuzzy oversampling. Forexample, for "feature envy", we were able to achieve 99+\% AUC across all ourdatasets, and on 8/10 datasets for "misplaced class". While our specificresults refer to code smell detection, they do suggest other lessons for otherkinds of analytics. For example: (a) try better preprocessing before tryingcomplex learners (b) include simpler learners as a baseline in softwareanalytics (c) try "fuzzy sampling" as one such baseline.

How to Improve Deep Learning for Software Analytics (a case study with code smell detection)