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On the relative value of imbalanced learning for code smell detection

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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The impact of feature selection and feature reduction techniques for code smell detection: A comprehensive empirical study

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CBReT: A Cluster-Based Resampling Technique for dealing with imbalanced data in code smell prediction

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Deconstructing oversampling in software defect prediction: Algorithm constraints, trade-offs, and new baselines

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Impact of Co‐Occurrences of Code Smells and Design Patterns on Internal Code Quality Attributes

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Smell-ML: A Machine Learning Framework for Detecting Rarely Studied Code Smells

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