Machine learning‐based code smell detection (CSD) has been demonstrated to be a
valuable approach for improving software quality and enabling developers to identify …

Abstract Code Smell Detection (CSD) plays a crucial role in improving software quality and
maintainability. And Deep Learning (DL) techniques have emerged as a promising …

Context Code smell detection is the process of identifying poorly designed and implemented
code pieces. Machine learning-based approaches require enormous amounts of manually …

Code smells indicate design issues that violate basic design principles such as hierarchy
encapsulation, abstraction, and others, potentially affecting software quality [1],[2]. Detecting …

In the context of limited maintenance resources, predicting the severity of code smells is
more practically useful than simply detecting them. Fontana et al. first empirically …

Code smells are indicators of deeper problems in source code that affect the system
maintainability and evolution. Detecting code smells is crucial as a software maintenance …

Code smell refers to substandard design patterns in software's source code that may lead to
faults-prone implementation. Machine learning-based code smell prediction models suffer …

The process of identifying software bugs is of paramount importance as it ensures software
reliability and facilitates maintenance activities. The quality improvement process of software …

Code smells indicate potential symptoms or problems in software due to inefficient design or
incomplete implementation. These problems can affect software quality in the long-term …

(1) Background: Code smell is the most popular and reliable method for detecting potential
errors in code. In real-world circumstances, a single source code may have multiple code …