Machine learning-based code smell detection method for No Low Memory Resolver
Code smell refers to poor code design or implementation that affects the code mainte-nance process and lowers software quality.Therefore,code smell detection is crucial in software refactoring.In this paper,we utilize five traditional machine learning models to detect Android-specific code smells.To acquire the large dataset required for machine learning models,we con-struct a Java code smell dataset comprising 14,000 samples,with 72 features extracted from the source code.Additionally,we conduct experimental validation using open-source Android appli-cations.The results show that Random Forest is the best-performing model for detecting code smells in No Low Memory Resolver,achieving the highest F1 score of 0.928.
NETL
NSTL
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