We conducted a systematic literature review of the most recent studies to survey the existing
ML-based methods for code smell detection. A systematic search of well-established digital …

Background: Code smells indicate suboptimal design or implementation choices in the
source code that often lead it to be more change-and fault-prone. Researchers defined …

Detecting code smells using machine learning presents inherent challenges due to the
unbalanced nature of the problem and susceptibility to interpretation biases. The objective of …

Context Code smells in the software systems are indications that usually correspond to
deeper problems that can negatively influence software quality characteristics. This review is …

Code smells are indicators of deeper problems in source code that affect the system
maintainability and evolution. Detecting code smells is crucial as a software maintenance …

Supervised learning-based code smell detection has become one of the dominant
approaches to identify code smell. Existing works optimize the process of code smell …

Context: A code smell indicates a flaw in the design, implementation, or maintenance
process that could degrade the software's quality and potentially cause future disruptions …

Code smells can compromise software quality in the long term by inducing technical debt.
For this reason, many approaches aimed at identifying these design flaws have been …

Code smells violate software development principles that make the software more prone to
errors and changes. Researchers have developed code smell detectors using manual and …

Code smells are poorly designed code structures indicating that the code may need to be
refactored. Recognizing code smells in practice is complex, and researchers strive to …