Code smells in software indicate poor design and implementation choices. Detecting and
removing them is critical for sustainable software development. Machine learning (ML) can …

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 …

Code smells are structures in code that often harm its quality. Manually detecting code
smells is challenging, so researchers proposed many automatic detectors. Traditional code …

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 …

Code smells slow down software system development and makes them harder to maintain.
Existing research aims to develop automatic detection algorithms to reduce the labor and …

Context Code smell detection is the process of identifying poorly designed and implemented
code pieces. Machine learning-based approaches require enormous amounts of manually …

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 common in poorly designed software that can hinder code maintainability.
Automatic detection of design flaws assists developers in identifying code smells in …

The term" code smell" refers to an indication of a problem with the quality of source code.
Numerous studies have been conducted to identify problematic features in source code …

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 …