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 are structures in code that often harm its quality. Manually detecting code
smells is challenging, so researchers proposed many automatic detectors. Traditional code …

Code smells are poorly designed code structures indicating that the code may need to be
refactored. Recognizing code smells in practice is complex, and researchers strive to …

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 smells in software indicate poor design and implementation choices. Detecting and
removing them is critical for sustainable software development. Machine learning (ML) can …

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 …

Code smell is the structural design defect that makes programs difficult to understand,
maintain, and evolve. Existing works of code smell detection mainly focus on prevalent 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 are poorly designed parts of code whose removal is essential for sustainable
software development. However, recognizing code smells in practice is challenging …

The presence of code smells will increase the risk of failure, make software difficult to
maintain, and introduce potential technique debt in the future. Although many deep-learning …