English(EN) Stack Trace-Based Crash Deduplication with Transformer Adaptation

Transformer模型dedupT增强堆栈跟踪崩溃去重能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-28 04:00

研究人员开发了一种新方法dedupT，用于在软件开发中自动识别和合并重复的崩溃报告。该方法利用Transformer模型，将其从自然语言处理领域适配到理解堆栈跟踪中的上下文和结构关系。在真实数据集上的实验表明，dedupT在对重复崩溃进行排序和检测唯一崩溃方面，显著优于现有的深度学习和传统方法，从而减少了手动分类的工作量。 AI

影响通过减少崩溃报告分类的手动工作量，提高软件开发效率。

排序理由这是一篇关于使用AI进行软件工程新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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Signal score: 24 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 这是一篇关于使用AI进行软件工程新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Md Afif Al Mamun, Gias Uddin, Lan Xia, Longyu Zhang · 2026-08-28 04:00

基于堆栈跟踪的崩溃去重与Transformer适配

arXiv:2508.19449v2 Announce Type: replace-cross Abstract: Automated crash reporting systems generate large volumes of duplicate reports, overwhelming issue-tracking systems and increasing developer workload. Traditional stack trace-based deduplication methods---relying on string …

本文如何被排名

报道来源 [1]

基于堆栈跟踪的崩溃去重与Transformer适配

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