A survey of automated techniques for detecting software code smells

Software code smells indicate potential design flaws that can affect software quality. This paper surveys automated techniques used to detect these code smells, categorizing them based on their approach (e.g., metrics-based, rule-based, machine learning) and discussing their strengths and weaknesses. It provides a comprehensive overview of the field, useful for researchers and practitioners interested in improving software quality through automated code smell detection, a key component of tools like SonarQube.