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.

Software deffect prediction, a problem of major relevance within the search-based software engineering field, aims to enhance software quality by early and precisely uncovering faulty software modules. Accurate detection of software defects in new software releases might increase the performance of the software development process in terms of cost, time and software quality. Most approaches from the software deffect prediction literature try to develop general solutions that are designed to work with any type of software deffect. From a software engineering perspective, software defects may take various forms and identifying/fixing different types of defects requires different approaches. Starting from the assumption that specific types of software defects have a particular behaviour, we are introducing in this paper, as a proof of concept, an unsupervised learning-based methodology for mining behavioural patterns for specific classes of software defects and identifying features which would be relevant for detecting the uncovered classes. The experiments performed on an open-source software deffect prediction data set collected from all releases of the Apache Ivy software highlight that the results obtained by applying the proposed methodology are highly correlated with the way human domain experts categorise and address software defects. Creating software deffect prediction models that are specifically tailored for different software deffect types may improve the accuracy of the developed models, open the possibility to apply different sets of predictive models based on the domain of the software and may accelerate the adoption of software deffect prediction approaches by the industry.