Integrating Lightweight Transformers for Cross-Project Bug Severity Classification: An Applied AI Approach in Software Engineering

Bug severity classification is an important task in software maintenance because it supports bug triage and resource allocation. However, newly created or evolving projects often lack sufficient labeled data, making cross-project severity prediction challenging due to domain shift and class imbalance. In this paper, we investigate cross-project bug severity classification using lightweight transformer models under practical deployment constraints. Specifically, DistilBERT and TinyBERT are employed and evaluated within a unified cross-project learning framework. Experiments are conducted on large-scale Mozilla bug repositories under both single-source and multi-source transfer settings. Macro-averaged F1 is used as the primary evaluation metric to ensure balanced assessment across severity levels. The results indicate that cross-project performance is strongly influenced by source–target pairing, reflecting the impact of domain shift. Multi-source training generally improves performance across several transfer scenarios, particularly for minority severity classes, although the improvements remain moderate. DistilBERT achieves higher overall performance, whereas TinyBERT shows comparable trends with only a small reduction in Macro-F1, suggesting a favorable trade-off between predictive performance and model efficiency. These findings suggest that lightweight transformer models can support practical bug triage processes by providing relatively consistent and computationally efficient severity predictions across projects, particularly in environments with limited labeled data and computational resources.