Identifying code smells in projects is a non-trivial task, and it is often a subjective activity
since developers have different understandings about them. The use of machine learning …

Researchers apply machine-learning techniques for code smell detection to counter the
subjectivity of many code smells. Such approaches need a large, manually annotated …

Code smells are poor implementation choices that developers apply while evolving source
code and that affect program maintainability. Multiple automated code smell detectors have …

Code smells are poor implementation choices that developers apply while evolving source
code and that affect program maintainability. Multiple automated code smell detectors have …

Code smells are considered symptoms of poor implementation choices, which may hamper
the software maintainability. Hence, code smells should be detected as early as possible to …

Code smells are symptoms of poor design that hamper software evolution and maintenance.
Hence, code smells should be detected as early as possible to avoid software quality …

Code smells are structures in code that present potential software maintainability issues.
Manually constructing high-quality datasets to train ML models for code smell detection is …

Code smells are sub-optimal design and implementation choices that hinder software
maintainability. Although significant progress has been achieved in code smell detection …

Code smells are poor implementation choices applied during software evolution that can
affect source code maintainability. While several heuristic-based approaches have been …

Code smells are inherently subjective in nature. Software developers may have different
opinions and perspectives on smelly code. While many attempts have been made to use …