# CUPID: Leveraging ChatGPT for More Accurate Duplicate Bug Report Detection

arXiv: https://arxiv.org/abs/2308.10022 · HTML: https://mlsanity.com/paper/2308.10022/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2023.

## Built on by (most cited first, 9 of 9)

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- [Integrating Large Language Models with Internet of Things Applications](https://mlsanity.com/paper/2410.19223/) (2024)
- [S3LLM: Large-Scale Scientific Software Understanding with LLMs using Source, Metadata, and Document](https://mlsanity.com/paper/2403.10588/) (2024)
- [BugBlitz-AI: An Intelligent QA Assistant](https://mlsanity.com/paper/2406.04356/) (2024)
- [Automated Duplicate Bug Report Detection in Large Open Bug Repositories](https://mlsanity.com/paper/2504.14797/) (2025)
- [Multi-CoLoR: Context-Aware Localization and Reasoning across Multi-Language Codebases](https://mlsanity.com/paper/2602.19407/) (2026)

## Abstract (arXiv)

Duplicate bug report detection (DBRD) is a long-standing challenge in both academia and industry. Over the past decades, researchers have proposed various approaches to detect duplicate bug reports more accurately. With the recent advancement of deep learning, researchers have also proposed several deep learning-based approaches to address the DBRD task. In the bug repositories with many bug reports, deep learning-based approaches have shown promising performance. However, in the bug repositories with a smaller number of bug reports, i.e., around 10k, the existing deep learning approaches show worse performance than the traditional approaches. Traditional approaches have limitations, too, e.g., they are usually based on the bag-of-words model, which cannot capture the semantics of bug reports. To address these aforementioned challenges, we seek to leverage a state-of-the-art large language model (LLM) to improve the performance of the traditional DBRD approach. In this paper, we propose an approach called CUPID, which combines the bestperforming traditional DBRD approach (i.e., REP) with the state-of-the-art LLM (i.e., ChatGPT). We conducted an evaluation by comparing CUPID with three existing approaches on three datasets. The experimental results show that CUPID achieves state-of-theart results, reaching Recall Rate@10 scores ranging from 0.602 to 0.654 across all the datasets analyzed. In particular, CUPID improves over the prior state-ofthe-art approach by 5% - 8% in terms of Recall Rate@10 in the datasets. CUPID also surpassed the state-of-the-art deep learning-based DBRD approach by up to 82%.