Code smells is the term used to signal certain patterns or structures in software code that
may contain a potential design or architecture problem, leading to maintainability or other …

Context: An excessive number of code smells make a software system hard to evolve and
maintain. Machine learning methods, in addition to metric-based and heuristic-based …

Code smells indicate potential symptoms or problems in software due to inefficient design or
incomplete implementation. These problems can affect software quality in the long-term …

Code smell refers to poor design that is perceived to have a negative impact on readability
and maintainability during software evolution, and it implies the possibility of refactoring …

In recent years, many studies on detecting code smells in source code have published
datasets with limited characteristics, such as the ambiguity of code smell definitions leads to …

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 structures in the source code that suggest the possibility of refactorings.
Consequently, developers may identify refactoring opportunities by detecting code smells …

Context: Code smell detection is the process of identifying code pieces that are poorly
designed and implemented. Recently more research has been directed towards machine …

Several approaches are proposed to detect code smells. A set of important approaches are
based on machine learning algorithms, which require the code smells have been labeled in …

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 …