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 …

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 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 …

Prior works have developed transformer-based language learning models to automatically
generate source code for a task without compilation errors. The datasets used to train these …

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

When developers make changes to their code, they typically run regression tests to detect if
their recent changes (re) introduce any bugs. However, many tests are flaky, and their …

Software maintainability predicts changes or failures that may occur in software after it has
been deployed. Since it deals with the degree to which an application may be understood …

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 …