The field of software development is moving towards improved code smell detection and software maintainability. Researchers are focusing on developing novel taxonomies and classification schemes for code smell interactions, as well as automated detection techniques for inline code comment smells. The use of machine learning models and large language models is becoming increasingly popular in this area. Studies have shown that providing information about implemented algorithms can improve program comprehension, and that automatic comment generation techniques can fall short due to method dependencies. As a result, researchers are exploring dependency-aware approaches for method-level comment generation. Additionally, comparative studies are being conducted to analyze performance smells in ML and non-ML projects, highlighting the need for targeted optimizations. Noteworthy papers include: A Novel Taxonomy and Classification Scheme for Code Smell Interactions, which presents a novel taxonomy for code smell interactions. Towards Automated Detection of Inline Code Comment Smells, which achieves a high accuracy in detecting inline code comment smells using machine learning models. Do Automatic Comment Generation Techniques Fall Short, which proposes a novel dependency-aware technique for method-level comment generation. Performance Smells in ML and Non-ML Python Projects, which provides a comparative analysis of performance smells between ML and non-ML projects.