一句话：提出 DeepLSH，用孪生网络学习局部敏感哈希，实现崩溃报告的快速近重复检索。
自动崩溃分桶是软件缺陷分诊的关键环节，通常用聚类将相似报告分组。但在实时流式缺陷收集下，系统需快速找到与新报告最相似的近重复项。最近邻搜索尤其是局部敏感哈希（LSH）因亚线性性能与精度保证适合大规模数据，但为先进崩溃分桶度量推导满足局部敏感性质的哈希函数并不容易，故 LSH 尚未用于该任务。本文提出 DeepLSH：一种带原创损失函数的孪生深度神经网络，可近似满足局部敏感性质，并适用于 Jaccard、Cosine 等度量。作者在公开新数据集上实验验证了该方法。
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.
DOI 原文 ·
@inproceedings{paperbot11295,
title = {DeepLSH: Deep Locality-Sensitive Hash Learning for Fast and Efficient Near-Duplicate Crash Report Detection},
author = {Youcef Remil and Anes Bendimerad and Romain Mathonat and Chedy Raïssi and Mehdi Kaytoue},
booktitle = {ICSE},
year = {2024},
doi = {10.1145/3597503.3639146}
}