# DeepLSH: Deep Locality-Sensitive Hash Learning for Fast and Efficient Near-Duplicate Crash Report Detection

arXiv: https://arxiv.org/abs/2310.06703 · HTML: https://mlsanity.com/paper/2310.06703/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 43 references that are in the map.
- First posted on arXiv 10 Oct 2023.

## Builds on (most cited first, 9 of 9)

- [A Survey on Deep Hashing Methods](https://mlsanity.com/paper/2003.03369/) (2020)
- [Simultaneous Feature Learning and Hash Coding with Deep Neural Networks](https://mlsanity.com/paper/1504.03410/) (2015)
- [Deep Supervised Discrete Hashing](https://mlsanity.com/paper/1705.10999/) (2017)
- [Hashing for Similarity Search: A Survey](https://mlsanity.com/paper/1408.2927/) (2014)
- [Deep Supervised Hashing with Triplet Labels](https://mlsanity.com/paper/1612.03900/) (2016)
- [Hashing as Tie-Aware Learning to Rank](https://mlsanity.com/paper/1705.08562/) (2017)
- [Learning to Hash with Binary Deep Neural Network](https://mlsanity.com/paper/1607.05140/) (2016)
- [TraceSim: A Method for Calculating Stack Trace Similarity](https://mlsanity.com/paper/2009.12590/) (2020)
- [Senatus -- A Fast and Accurate Code-to-Code Recommendation Engine](https://mlsanity.com/paper/2111.04473/) (2021)

## Built on by (most cited first, 3 of 3)

- [AIOps Solutions for Incident Management: Technical Guidelines and A Comprehensive Literature Review](https://mlsanity.com/paper/2404.01363/) (2024)
- [Exploiting Formal Concept Analysis for Data Modeling in Data Lakes](https://mlsanity.com/paper/2408.13265/) (2024)
- [Stack Trace-Based Crash Deduplication with Transformer Adaptation](https://mlsanity.com/paper/2508.19449/) (2025)

## Abstract (arXiv)

Automatic crash bucketing is a crucial phase in the software development process for efficiently triaging bug reports. It generally consists in grouping similar reports through clustering techniques. However, with real-time streaming bug collection, systems are needed to quickly answer the question: What are the most similar bugs to a new one?, that is, efficiently find near-duplicates. It is thus natural to consider nearest neighbors search to tackle this problem and especially the well-known locality-sensitive hashing (LSH) to deal with large datasets due to its sublinear performance and theoretical guarantees on the similarity search accuracy. Surprisingly, LSH has not been considered in the crash bucketing literature. It is indeed not trivial to derive hash functions that satisfy the so-called locality-sensitive property for the most advanced crash bucketing metrics. Consequently, we study in this paper how to leverage LSH for this task. To be able to consider the most relevant metrics used in the literature, we introduce DeepLSH, a Siamese DNN architecture with an original loss function, that perfectly approximates the locality-sensitivity property even for Jaccard and Cosine metrics for which exact LSH solutions exist. We support this claim with a series of experiments on an original dataset, which we make available.