Understanding Code Smell Detection Through Hyperparameter Optimization and Metric Correlation Analysis

Hyperparameter optimization plays a pivotal role in the reliability and generalization of machine-learning models for software quality prediction. This paper presents a comparative evaluation of three search strategies: Grid Search, Random Search, and a Hybrid approach applied to the detection of four types of code smells (BLOB, Functional Decomposition, Spaghetti Code, and Swiss Army Knife). Experiments were performed on three open-source Java systems (Azureus, ArgoUML, and Xerces) using Support Vector Machines (SVMs). The results show that all three strategies produce consistent patterns in accuracy and F1-score across the datasets, confirming the robustness of the evaluation framework. Simultaneously, Random Search tends to achieve higher recall for the Functional Decomposition smell, reflecting its ability to explore sparse and irregular hyperparameter spaces. The Hybrid approach combines the exploratory strength of Random Search with the local refinement of Grid Search, offering greater stability with a moderate computational cost. A correlation analysis of software metrics further reveals that while the Blob smell is consistently associated with size and coupling indicators, the other smells—Functional Decomposition, Spaghetti Code, and Swiss Army Knife are captured more indirectly through general structural proxies such as size, cohesion, and coupling. Overall, these findings suggest that hyperparameter tuning not only enhances predictive performance but also provides valuable insights into the structural factors underlying code smells.