Abstract: Software defect prediction plays a crucial role in improving software quality, yet existing approaches still suffer from severe class imbalance, redundant feature spaces, weak generalization, and limited interpretability, making their adoption in real development pipelines difficult. Many current models rely on black-box deep learning architectures or conventional classifiers that fail to identify minority defects or explain the reasoning behind their decisions. To overcome these limitations, this study introduces a novel framework named Contrastive Siamese Defect Learning–Integrated Explainable Neural Optimization System (CSDL-SEN-XAI), which integrates contrastive metric learning, enzyme-inspired optimization, and transparent explainability. The method combines SMOTE-based balancing, the Enzyme Action Optimizer for joint feature–hyperparameter optimization, and a Siamese Neural Network trained using contrastive loss to learn discriminative similarity embeddings. The entire workflow is implemented using Python, enabling efficient scalability and reproducibility. Experimental analysis reveals that the proposed model achieves an accuracy of 95.5%, a recall of 96.2%, and an F1-score of 95.5%, outperforming traditional models such as Random Forest, SVM, and CNN by margins ranging from 7% to 15% under identical evaluation settings. SHAP and Integrated Gradients further demonstrate that the model provides clear global and instance-level explanations, highlighting influential software metrics and strengthening the interpretability of predictions. Overall, the results confirm that CSDL-SEN-XAI delivers superior predictive performance, stable optimization, balanced learning, and transparent defect interpretation, offering a reliable and interpretable solution suitable for practical software engineering environments. Future work will explore cross-project defect prediction and the integration of lightweight optimization strategies to further enhance scalability.

Srinivasa Rao Katragadda and Sirisha Potluri. “An Interpretable Analytical Intelligence Architecture Delivering Reliable Detection of Software Defect Instances”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.12 (2025). http://dx.doi.org/10.14569/IJACSA.2025.01612103

@article{Katragadda2025,
title = {An Interpretable Analytical Intelligence Architecture Delivering Reliable Detection of Software Defect Instances},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.01612103},
url = {http://dx.doi.org/10.14569/IJACSA.2025.01612103},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {12},
author = {Srinivasa Rao Katragadda and Sirisha Potluri}
}