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 …

Refactoring detection is crucial for a variety of applications and tasks:(i) empirical studies
about code evolution,(ii) tools for library API migration,(iii) code reviews and change …

Refactoring detection algorithms have been crucial to a variety of applications:(i) empirical
studies about the evolution of code, tests, and faults,(ii) tools for library API migration,(iii) …

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 …

Artificial intelligence (AI) and machine learning (ML) are increasingly broadly adopted in
industry. However, based on well over a dozen case studies, we have learned that …

The metaphor of technical debt was introduced to express the trade off between productivity
and quality, ie, when developers take shortcuts or perform quick hacks. More recently, our …

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

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