A Systematic Review on Code Smell Detection Approaches in Open Source Projects

ABSTRACT Background Code smells are an indicator that something is misplaced in software systems, which can reduce maintainability and quality of software applications. In the literature, there is much research about code smells where numerous strategies for automating code smell detection have been developed to improve software quality. Objective The purpose of this work is to provide a review of search‐based, heuristic‐based, machine learning‐based, deep learning‐based, and hybrid‐based code smell detection algorithms. Method Concerning the main goals of this research, we have found 38 primary studies. We gathered relevant studies published on this topic between 2017 and 2024. These articles' data were extracted according to some criteria, including code smells, machine learning methods, programming languages, dataset size, evaluation strategy, and statistical tests. Results Machine learning‐based code smell detection methods have been suggested in most empirical investigations. We found that machine learning and deep learning are the most popular approaches for predicting code smells. Researchers typically employ support vector machine and decision tree algorithms. The Arcelli Fontana and Zanoni benchmark dataset was the most frequently investigated dataset. Most of the research community's attention has been focused on a small number of smells, including blob, feature envy, long method, and data class. Researchers also pay more attention to code smells like Long Method and Feature Envy. Deep learning techniques are increasingly used, and most scientists utilize source code metrics as indicators. The standard performance measurements mostly used are F‐measure, recall, and precision. Conclusions The study provides an overview of existing approaches and highlights current research trends in code smell detection, particularly the increasing use of machine learning and deep learning techniques.