# Duplicate Bug Report Detection: How Far Are We?

arXiv: https://arxiv.org/abs/2212.00548 · HTML: https://mlsanity.com/paper/2212.00548/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2022.

## Built on by (most cited first, 8 of 8)

- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [When LLMs Meet Cybersecurity: A Systematic Literature Review](https://mlsanity.com/paper/2405.03644/) (2024)
- [ChatGPT: A Study on its Utility for Ubiquitous Software Engineering Tasks](https://mlsanity.com/paper/2305.16837/) (2023)
- [Learning Software Bug Reports: A Systematic Literature Review](https://mlsanity.com/paper/2507.04422/) (2025)
- [Semantic GUI Scene Learning and Video Alignment for Detecting Duplicate Video-based Bug Reports](https://mlsanity.com/paper/2407.08610/) (2024)
- [Automated Duplicate Bug Report Detection in Large Open Bug Repositories](https://mlsanity.com/paper/2504.14797/) (2025)
- [Refining GPT-3 Embeddings with a Siamese Structure for Technical Post Duplicate Detection](https://mlsanity.com/paper/2312.15068/) (2023)
- [A Defect Classification Framework for AI-Based Software Systems (AI-ODC)](https://mlsanity.com/paper/2508.17900/) (2025)

## Abstract (arXiv)

Many Duplicate Bug Report Detection (DBRD) techniques have been proposed in the research literature. The industry uses some other techniques. Unfortunately, there is insufficient comparison among them, and it is unclear how far we have been. This work fills this gap by comparing the aforementioned techniques. To compare them, we first need a benchmark that can estimate how a tool would perform if applied in a realistic setting today. Thus, we first investigated potential biases that affect the fair comparison of the accuracy of DBRD techniques. Our experiments suggest that data age and issue tracking system choice cause a significant difference. Based on these findings, we prepared a new benchmark. We then used it to evaluate DBRD techniques to estimate better how far we have been. Surprisingly, a simpler technique outperforms recently proposed sophisticated techniques on most projects in our benchmark. In addition, we compared the DBRD techniques proposed in research with those used in Mozilla and VSCode. Surprisingly, we observe that a simple technique already adopted in practice can achieve comparable results as a recently proposed research tool. Our study gives reflections on the current state of DBRD, and we share our insights to benefit future DBRD research.