FS-iSDP: A few-shot learning approach using siamese networks for interpretable software defect prediction

Software defect prediction (SDP) plays a critical role in improving software quality by identifying defective components during development and maintenance. This paper introduces FS-iSDP, an interpretable few-shot learning approach based on Siamese Neural Networks, designed to address the challenges posed by highly imbalanced SDP datasets. The proposed model learns to measure the similarity between software application classes represented through software metric-based features, enabling effective defect classification with limited training examples. Experiments were conducted on the Apache Calcite open-source project using a cross-version setup that simulates realistic software evolution. The results show that FS-iSDP achieves high recall and strong AUC values across multiple software versions, even as the number of defects decreases and class imbalance intensifies. To improve performance and reduce computational costs, Univariate Feature Selection is applied, which leads to improved precision and critical success index, along with a significant reduction in training time. In addition, a comparative evaluation is performed against state-of-the-art SDP methods, showing that FS-iSDP achieves competitive performance in most metrics. Finally, LIME analysis is used to interpret the model predictions, offering insight into which features most influence defect classification decisions. This approach proves effective for realistic, evolving software systems, and provides both predictive capabilities and interpretability in scenarios with scarce defect labels.