A Systematic Literature Review on the Code Smells Datasets and Validation Mechanisms

The accuracy reported for code smell-detecting tools varies depending on the dataset used to evaluate tools. Our survey of 45 existing datasets reveals that adequacy a detecting smells highly depends relevant properties such as size, severity level, project types, number each type smell, smells, and ratio smelly non-smelly samples in dataset. Most support God Class, Long Method, Feature Envy, while six Fowler Beck's catalog are not supported by any datasets. We conclude suffer from imbalanced samples, lack supporting restriction Java language.