Evaluating Fine-tuning Approaches for Duplicate Bug Report Detection

Bug reports are artefacts that document defects encountered by users or developers. Rapid testing and release cycles often lead to the creation of similar or near-duplicate bug reports, introducing redundancy, delaying triage, and increasing maintenance overhead. Although prior research has extensively explored automated methods to detect and manage duplicate bug reports, their natural language nature makes recent advances in large language models (LLMs)—particularly BERT and its successors—a promising avenue for improving robustness and accuracy. In this study, we investigate the use of LLMs to identify duplicate bug reports (DBRs), focusing on the impact of fine-tuning an all-mpnet-base-v2 model, which builds on BERT-based architectures while addressing several of their limitations. We fine-tuned the model using large, open-source bug tracking datasets from the Eclipse, OpenOffice, Firefox, and NetBeans projects. Our evaluation shows that fine-tuning yields only marginal performance improvements across all datasets. We also discuss the trade-offs involved in fine-tuning LLMs for this task, including hyperparameter tuning guidelines and the practical challenges posed by computational and financial cost.