Actionable code smell identification with fusion learning of metrics and semantics

Code smell detection is one of the essential tasks in the field of software engineering. Identifying whether a code snippet has a code smell is subjective and varies by programming language, developer, and development method. Moreover, developers tend to focus on code smells that have a real impact on development and ignore insignificant ones. However, existing static code analysis tools and code smell detection approaches exhibit a high false positive rate in detecting code smells, which makes insignificant smells drown out those smells that developers value. Therefore, accurately reporting those actionable code smells that developers tend to spend energy on refactoring can prevent developers from getting lost in the sea of smells and improve refactoring efficiency. In this paper, we aim to detect actionable code smells that developers tend to refactor. Specifically, we first collect actionable and non-actionable code smells from projects with numerous historical versions to construct our datasets. Then, we propose a dual-stream model for fusion learning of code metrics and code semantics to detect actionable code smells. On the one hand, code metrics quantify the code's structure and even some rules or patterns, providing fundamental information for detecting code smells. On the other hand, code semantics encompass information about developers' refactoring tendencies, which prove valuable in detecting actionable code smells. Extensive experiments show that our approach can detect actionable code smells more accurately compared to existing approaches.