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.

本文研究了如何利用局部敏感哈希（LSH）来解决软件开发过程中自动故障分组的问题，并介绍了 DeepLSH，一种 Siamese DNN 架构和原始损失函数，以逼近 Jaccard 和 Cosine 度量的局部敏感性特性，并通过一系列实验验证了这一方法。

DeepLSH：深度局部敏感哈希学习用于快速高效的近似崩溃报告检测

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

Nearest neighbors search is a fundamental problem in various research fields
like machine learning, data mining and pattern recognition. Recently,
hashing-based approaches, e.g., Locality Sensitive Hashing (LSH), are proved to
be effective for scalable high dimensional nearest neighbors search. Many
hashing algorithms found their theoretic root in random projection. Since these
algorithms generate the hash tables (projections) randomly, a large number of
hash tables (i.e., long codewords) are required in order to achieve both high
precision and recall. To address this limitation, we propose a novel hashing
algorithm called {\em Density Sensitive Hashing} (DSH) in this paper. DSH can
be regarded as an extension of LSH. By exploring the geometric structure of the
data, DSH avoids the purely random projections selection and uses those
projective functions which best agree with the distribution of the data.
Extensive experimental results on real-world data sets have shown that the
proposed method achieves better performance compared to the state-of-the-art
hashing approaches.

提出了一种名为密度敏感哈希（DSH）算法，它是局部敏感哈希（LSH）的扩展，利用数据的几何结构避免了纯随机投影的限制，并在大规模高维数据搜索中取得更好的性能。