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Duplicate bug report detection (DBRD) is a long-standing challenge in bothacademia and industry. Over the past decades, researchers have proposed variousapproaches to detect duplicate bug reports more accurately. With the recentadvancement of deep learning, researchers have also proposed several approachesthat leverage deep learning models to detect duplicate bug reports. A recentbenchmarking study on DBRD also reveals that the performance of deeplearning-based approaches is not always better than the traditional approaches.However, traditional approaches have limitations, e.g., they are usually basedon the bag-of-words model, which cannot capture the semantics of bug reports.To address these aforementioned challenges, we seek to leveragestate-of-the-art large language model to improve the performance of thetraditional DBRD approach.  In this paper, we propose an approach called Cupid, which combines thebest-performing traditional DBRD approach REP with the state-of-the-art largelanguage model ChatGPT. Specifically, we first leverage ChatGPT under thezero-shot setting to get essential information on bug reports. We then use theessential information as the input of REP to detect duplicate bug reports. Weconducted an evaluation on comparing Cupid with three existing approaches onthree datasets. The experimental results show that Cupid achieves newstate-of-the-art results, reaching Recall Rate@10 scores ranging from 0.59 to0.67 across all the datasets analyzed. Our work highlights the potential ofcombining large language models to improve the performance of softwareengineering tasks.

Cupid: Leveraging ChatGPT for More Accurate Duplicate Bug Report Detection