  In the realm of Duplicate Bug Report Detection (DBRD), conventional methods
primarily focus on statically analyzing bug databases, often disregarding the
running time of the model. In this context, complex models, despite their high
accuracy potential, can be time-consuming, while more efficient models may
compromise on accuracy. To address this issue, we propose a transformer-based
system designed to strike a balance between time efficiency and accuracy
performance. The existing methods primarily address it as either a retrieval or
classification task. However, our hybrid approach leverages the strengths of
both models. By utilizing the retrieval model, we can perform initial sorting
to reduce the candidate set, while the classification model allows for more
precise and accurate classification. In our assessment of commonly used models
for retrieval and classification tasks, sentence BERT and RoBERTa outperform
other baseline models in retrieval and classification, respectively. To provide
a comprehensive evaluation of performance and efficiency, we conduct rigorous
experimentation on five public datasets. The results reveal that our system
maintains accuracy comparable to a classification model, significantly
outperforming it in time efficiency and only slightly behind a retrieval model
in time, thereby achieving an effective trade-off between accuracy and
efficiency.
