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Log-based anomaly detection for evolving software: An incremental deep-learning approach

delete2026-05-24
delete0
PRE
AI
X
Xinjie Wei
孙
孙昌爱 (Chang‐ai Sun) *
X
Xiao–Yi Zhang
D
Dave Towey
DOI：10.1016/j.infsof.2026.108199delete
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摘要

摘要

En 中文
DevOps——一种将软件开发与运维相结合的集成方法——已被广泛应用于各种长期运行的软件系统中。这些系统的一个关键问题是，任何运行时异常都必须被及时观察和处理。基于日志的异常检测（LAD）技术已被提出。LAD技术使用从日志中提取的特征，检查持续生成的日志是否与历史正常日志匹配。然而，此类系统的持续开发不可避免地导致日志不断变化，不幸的是，这些变化往往被LAD技术所忽略——它们可能会产生大量错误的警报信息（异常检测中的误报）。
Keyword:
Log-based anomaly detection
DevOps
Software evolution
Deep learning
False positives

期刊

Information and Software Technology 封面图
Information and Software Technology
IF:
4.3
论文数:
3.8K
被引数:
7.7K

机构

U
university of nottingham
学者数:
3.8K
论文数: 1.8K
被引数: 0
U
university of science and technology beijing
学者数:
1.3W
论文数: 4.5K
被引数: 2
引用论文

引用论文

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An Incremental Approach for Understanding Collision Avoidance of an Industrial Path Planner
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err2009-10-11
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Log-Based Anomaly Detection With Robust Feature Extraction and Online Learning基于日志的鲁棒特征提取和在线学习的异常检测
errIEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
IF8
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Using Large-Scale Anomaly Detection on Code to Improve Kotlin Compiler
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err0
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Efficient Fault Detection and Diagnosis in Complex Software Systems with Information-Theoretic Monitoring
errIEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
IF7.5
err2011-07-01
err28
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errJiang, Miao; Munawar, Mohammad A.; Reidemeister, Thomas; Ward, Paul A. S.
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