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 …

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 …

Automatic static analysis tools (ASATs) are instruments that support code quality
assessment by automatically detecting defects and design issues. Despite their popularity …

Defect prediction models focus on identifying defect-prone code elements, for example to
allow practitioners to allocate testing resources on specific subsystems and to provide …

Software defect prediction (SDP) seeks to estimate fault-prone areas of the code to focus
testing activities on more suspicious portions. Consequently, high-quality software is …

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 …

Architectural smells can negatively impact on different software qualities and can represent
a relevant source of architectural debt. Several architectural smells have been defined by …

The accuracy reported for code smell-detecting tools varies depending on the dataset used
to evaluate the tools. Our survey of 45 existing datasets reveals that the adequacy of a …