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 …

Large Language Models (LLMs) have demonstrated remarkable performance on coding
related tasks, particularly on assisting humans in programming and facilitating programming …

The violation of the semantic and structural software principles, such as low connection, high
coherence, high understanding, and others, are called anti-patterns, which is one of the …

Code smells are structures in code that may indicate maintainability issues. They are
challenging to define, and software engineers detect them differently. Mitigation of this …

Code smells are sub-optimal design and implementation choices that hinder software
maintainability. Although significant progress has been achieved in code smell detection …

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

Code smells negatively impact software maintenance and several mitigation tools and/or
techniques have been devised in the past. However, their interpretation is subjective and …

Identifying code smells in projects is a non-trivial task, and it is often a subjective activity
since developers have different understandings about them. The use of machine learning …

Code smells are structures in code that present potential software maintainability issues.
Manually constructing high-quality datasets to train ML models for code smell detection is …

Code Smell detection is a crucial task in software systems. The code smell can negatively
impact software maintenance and evolution. The machine learning-based code smell …