Systematic literature review on software code smell detection approaches

Abstract

Software code smells, subtle indicators of potential design flaws, play a critical role in maintaining software quality and preventing future maintenance issues. Numerous researchers have proposed various tools and employed different machine learning and deep learning techniques to detect software code smells. This survey systematically reviews the work conducted on detecting software code smells through tool-based, ML-based, and DL-based approaches published from 2014 to 2024. The imbalanced nature of datasets is another vital issue in this domain, where instances of software code smells are often significantly underrepresented, which poses a substantial challenge for traditional detection techniques. Therefore, this review also includes efforts to detect software code smells using different imbalance learning techniques. After initial scrutiny and selection, a total of 86 studies are analyzed and reported in this review work, providing a comprehensive overview of the field. This work comprehensively analyzes the intersection between software code smell detection and imbalance learning techniques, highlighting challenges posed by imbalanced datasets. Furthermore, we identify the best-performing ML techniques (e.g., Random Forest, SVM), the most commonly detected code smells (e.g., God Class, Data Class, Long Method, and feature Envy), and popular experimental setup techniques (e.g., K-fold cross-validation) used in prior studies. Based on the analysis, several key challenges and research gaps are identified, offering directions for future research.