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 …

Measuring and evaluating source code similarity is a fundamental software engineering
activity that embraces a broad range of applications, including but not limited to code …

The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …

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 …

Code smells are poor implementation choices applied by developers during software
evolution that often lead to critical flaws or failure. Much in the same way, community smells …

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

Code smells detection helps in improving understandability and maintainability of software
while reducing the chances of system failure. In this study, six machine learning algorithms …

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 …