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ABSTRACT
We provide abstract of deep machine learning
Introduction: In software development, code smells are indicators of potential design issues that can lead to maintenance problems and reduced software quality. Detecting code smells early in the development process is crucial for ensuring code maintainability and scalability. Traditional methods of code smell detection rely on manual inspection, which can be time-consuming and error-prone. In recent years, machine learning techniques have shown promising results in automating code smell detection.
Machine Learning Approach: Utilizing machine learning algorithms for code smell detection involves training models on features extracted from source code. These features may include metrics such as code complexity, coupling, and cohesion. By analyzing these features, machine learning models can learn patterns associated with different types of code smells.
Feature Extraction: The process of feature extraction involves transforming raw source code into numerical representations that can be fed into machine learning algorithms.
Model Training: Once features are extracted, machine learning models such as support vector machines (SVM), decision trees. During training, the models learn to distinguish between clean and smelly code based on the extracted features.
Evaluation: Additionally, techniques such as cross-validation help ensure the robustness of the models by testing them on different subsets of the data.
Conclusion: Machine learning offers a promising approach to automating code smell detection, enabling developers to identify and address potential issues early in the development process. By integrating machine learning techniques into software development workflows, teams can improve code quality and maintainability, ultimately enhancing the overall software product.
