# Stack Trace Deduplication: Faster, More Accurately, and in More Realistic Scenarios

arXiv: https://arxiv.org/abs/2412.14802 · HTML: https://mlsanity.com/paper/2412.14802/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 19 Dec 2024.

## Builds on (most cited first, 14 of 14)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://mlsanity.com/paper/2004.12832/) (2020)
- [Passage Re-ranking with BERT](https://mlsanity.com/paper/1901.04085/) (2019)
- [Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring](https://mlsanity.com/paper/1905.01969/) (2019)
- [BERT with History Answer Embedding for Conversational Question Answering](https://mlsanity.com/paper/1905.05412/) (2019)
- [Rethinking InfoNCE: How Many Negative Samples Do You Need?](https://mlsanity.com/paper/2105.13003/) (2021)
- [Smoothed Contrastive Learning for Unsupervised Sentence Embedding](https://mlsanity.com/paper/2109.04321/) (2021)
- [TraceSim: A Method for Calculating Stack Trace Similarity](https://mlsanity.com/paper/2009.12590/) (2020)
- [S3M: Siamese Stack (Trace) Similarity Measure](https://mlsanity.com/paper/2103.10526/) (2021)
- [The IntelliJ Platform: a Framework for Building Plugins and Mining Software Data](https://mlsanity.com/paper/2110.00141/) (2021)
- [Aggregation of Stack Trace Similarities for Crash Report Deduplication](https://mlsanity.com/paper/2205.00212/) (2022)

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

- [Stack Trace-Based Crash Deduplication with Transformer Adaptation](https://mlsanity.com/paper/2508.19449/) (2025)

## Abstract (arXiv)

In large-scale software systems, there are often no fully-fledged bug reports with human-written descriptions when an error occurs. In this case, developers rely on stack traces, i.e., series of function calls that led to the error. Since there can be tens and hundreds of thousands of them describing the same issue from different users, automatic deduplication into categories is necessary to allow for processing. Recent works have proposed powerful deep learning-based approaches for this, but they are evaluated and compared in isolation from real-life workflows, and it is not clear whether they will actually work well at scale. To overcome this gap, this work presents three main contributions: a novel model, an industry-based dataset, and a multi-faceted evaluation. Our model consists of two parts - (1) an embedding model with byte-pair encoding and approximate nearest neighbor search to quickly find the most relevant stack traces to the incoming one, and (2) a reranker that re-ranks the most fitting stack traces, taking into account the repeated frames between them. To complement the existing datasets collected from open-source projects, we share with the community SlowOps - a dataset of stack traces from IntelliJ-based products developed by JetBrains, which has an order of magnitude more stack traces per category. Finally, we carry out an evaluation that strives to be realistic: measuring not only the accuracy of categorization, but also the operation time and the ability to create new categories. The evaluation shows that our model strikes a good balance - it outperforms other models on both open-source datasets and SlowOps, while also being faster on time than most. We release all of our code and data, and hope that our work can pave the way to further practice-oriented research in the area.