Treffer: Enhanced Fault Detection Using Coupling and Cohesion Metrics with Deep CNN Modeling.

Detecting faults in software modules is important for reducing system failures and improving software quality. Traditional fault prediction methods often rely on failure history or statistical models, which may not work well when structural complexities exist within the code. This work introduces a new model called Enhanced Coupling and Cohesion Metrics-based Fault Detection (ECCMFD). It uses deep structural properties of code, such as Conceptual Lack of Cohesion in Methods (C-LCOM) and Conceptual Coupling Between Object Classes (CCBO), to capture how components interact and how focused each class remains on its purpose. These metrics are passed into a Deep Convolutional Neural Network (Deep CNN) that learns patterns in software design and predicts fault-prone modules. The model is evaluated on standard NASA MDP datasets including KC1, CM1 and PC3. It outperforms widely used models like Baseline CNN, Random Forest (RF) and XGBoost in all key evaluation metrics. ECCMFD achieved a 5% improvement in precision, a reduction in Root Mean Square Error (RMSE) by 0.3, and better performance in F1 score and accuracy. This improvement is due to the combination of well-defined structural metrics and the deeper feature learning capability of the deep CNN architecture. [ABSTRACT FROM AUTHOR]

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