Comprehensive Review: Key metrics in defect prediction Models
Keywords:
Defect prediction models, metrics, line of code, code smellsAbstract
Defect prediction models are crucial in identifying potential issues within software systems. Numerous software quality models have been proposed and developed to assess and improve the quality of software products [1]. This article explores key metrics employed in defect prediction models, including Lines of Code, Cyclomatic Complexity, Code Churn, Code Coupling, Code Complexity Metrics, Code Smells, Test Metrics, Developer Collaboration Metrics, Historical Defect Density, Size of Changes, and Contextual Metrics. These metrics provide quantitative insights into code quality and defect proneness. Defective software modules cause software failures, increase development and maintenance costs, and decrease customer satisfaction [2]. However, challenges such as imbalanced datasets, evolving software projects, overfitting, context sensitivity, lack of standardization, and incorporating human factors need addressing. Evaluation metrics and validation techniques, including cross-validation and external validation, play a vital role in overcoming these challenges and improving the accuracy and applicability of defect prediction models.
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