1 research outputs found
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 the tools. Our survey of 45 existing datasets reveals
that the adequacy of a dataset for detecting smells highly depends on relevant
properties such as the size, severity level, project types, number of each type
of smell, number of smells, and the ratio of smelly to non-smelly samples in
the dataset. Most existing datasets support God Class, Long Method, and Feature
Envy while six smells in Fowler and Beck's catalog are not supported by any
datasets. We conclude that existing datasets suffer from imbalanced samples,
lack of supporting severity level, and restriction to Java language.Comment: 34 pages, 10 figures, 12 tables, Accepte