Code Metric

Code metrics quantify various aspects of software code, aiming to predict software quality attributes like defect proneness, understandability, and testability. Current research focuses on leveraging diverse data sources beyond traditional complexity metrics, including code content, class dependencies, and even developer style, often employing machine learning models like random forests, support vector machines, and neural networks for prediction. These advancements improve the accuracy and efficiency of software development by enabling earlier detection of potential issues and facilitating better code design, ultimately reducing costs and improving software reliability.