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

The presence of code smells will increase the risk of failure, make software difficult to
maintain, and introduce potential technique debt in the future. Although many deep-learning …

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 …

Code smells are symptoms of poor design and implementation choices weighing heavily on
the quality of produced source code. During the last decades several code smell detection …

Code smells are indicators of potential problems in software. They tend to have a negative
impact on software quality. Several studies use machine learning techniques to detect bad …

Code smells represent poor implementation choices performed by developers when
enhancing source code. Their negative impact on source code maintainability and …

Code smells are the result of not following software engineering principles during software
development, especially in the design and coding phase. It leads to low maintainability. 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 …

Code smells or bad smells are an accepted approach to identify design flaws in the source
code. Although it has been explored by researchers, the interpretation of programmers is …