Detecting code smells using machine learning presents inherent challenges due to the
unbalanced nature of the problem and susceptibility to interpretation biases. The objective of …

We conducted a systematic literature review of the most recent studies to survey the existing
ML-based methods for code smell detection. A systematic search of well-established digital …

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 …

Supervised learning-based code smell detection has become one of the dominant
approaches to identify code smell. Existing works optimize the process of code smell …

Code smell detection is critical for calculating system quality and identifying issues that
require more work and development. The technique of finding wrongly developed code …

Code smells detection primarily adopts heuristic-based, machine learning, and deep
learning approaches, However, to enhance accuracy, most studies employ deep learning …

Code smells can compromise software quality in the long term by inducing technical debt.
For this reason, many approaches aimed at identifying these design flaws have been …

Context: A code smell indicates a flaw in the design, implementation, or maintenance
process that could degrade the software's quality and potentially cause future disruptions …

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 …

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 …