# Automated Duplicate Bug Report Detection in Large Open Bug Repositories

arXiv: https://arxiv.org/abs/2504.14797 · HTML: https://mlsanity.com/paper/2504.14797/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 21 Apr 2025.

## Builds on (most cited first, 4 of 4)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [CUPID: Leveraging ChatGPT for More Accurate Duplicate Bug Report Detection](https://mlsanity.com/paper/2308.10022/) (2023)
- [Duplicate Bug Report Detection: How Far Are We?](https://mlsanity.com/paper/2212.00548/) (2022)

## Built on by (most cited first, 1 of 1)

- [When Bugs Linger: A Study of Anomalous Resolution Time Outliers and Their Themes](https://mlsanity.com/paper/2509.16140/) (2025)

## Abstract (arXiv)

Many users and contributors of large open-source projects report software defects or enhancement requests (known as bug reports) to the issue-tracking systems. However, they sometimes report issues that have already been reported. First, they may not have time to do sufficient research on existing bug reports. Second, they may not possess the right expertise in that specific area to realize that an existing bug report is essentially elaborating on the same matter, perhaps with a different wording. In this paper, we propose a novel approach based on machine learning methods that can automatically detect duplicate bug reports in an open bug repository based on the textual data in the reports. We present six alternative methods: Topic modeling, Gaussian Naive Bayes, deep learning, time-based organization, clustering, and summarization using a generative pre-trained transformer large language model. Additionally, we introduce a novel threshold-based approach for duplicate identification, in contrast to the conventional top-k selection method that has been widely used in the literature. Our approach demonstrates promising results across all the proposed methods, achieving accuracy rates ranging from the high 70%'s to the low 90%'s. We evaluated our methods on a public dataset of issues belonging to an Eclipse open-source project.