mlsanityData to 7 Oct 2026

A Survey on Automated Log Analysis for Reliability Engineering

arXiv 2009.07237 ↗First posted 15 Sep 2020cs.SEcited by 50

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Abstract (arXiv)

Logs are semi-structured text generated by logging statements in software source code. In recent decades, software logs have become imperative in the reliability assurance mechanism of many software systems because they are often the only data available that record software runtime information. As modern software is evolving into a large scale, the volume of logs has increased rapidly. To enable effective and efficient usage of modern software logs in reliability engineering, a number of studies have been conducted on automated log analysis. This survey presents a detailed overview of automated log analysis research, including how to automate and assist the writing of logging statements, how to compress logs, how to parse logs into structured event templates, and how to employ logs to detect anomalies, predict failures, and facilitate diagnosis. Additionally, we survey work that releases open-source toolkits and datasets. Based on the discussion of the recent advances, we present several promising future directions toward real-world and next-generation automated log analysis.

Key facts. Cited by 50 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Sep 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 3; 2023: 10; 2024: 13; 2025: 13; 2026: 7.

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  1. 2022Log-based Anomaly Detection with Deep Learning: How Far Are We?cited by 56
  2. 2021Experience Report: Deep Learning-based System Log Analysis for Anomaly Detectioncited by 37
  3. 2023LogGPT: Exploring ChatGPT for Log-Based Anomaly Detectioncited by 33
  4. 2024LLMParser: An Exploratory Study on Using Large Language Models for Log Parsingcited by 32
  5. 2024Face It Yourselves: An LLM-Based Two-Stage Strategy to Localize Configuration Errors via Logscited by 17
  6. 2024Lemur: Log Parsing with Entropy Sampling and Chain-of-Thought Mergingcited by 16
  7. 2023AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challengescited by 13
  8. 2021UniLog: Deploy One Model and Specialize it for All Log Analysis Taskscited by 11
  9. 2025Large Language Models for Security Operations Centers: A Comprehensive Surveycited by 9
  10. 2023Heterogeneous Anomaly Detection for Software Systems via Semi-supervised Cross-modal Attentioncited by 8
  11. 2021A Comprehensive Survey of Logging in Software: From Logging Statements Automation to Log Mining and Analysiscited by 6
  12. 2023On the Effectiveness of Log Representation for Log-based Anomaly Detectioncited by 6
38 more
  1. 2023Learning Representations on Logs for AIOpscited by 6
  2. 2024Demystifying and Extracting Fault-indicating Information from Logs for Failure Diagnosiscited by 6
  3. 2021A Taxonomy of Anomalies in Log Datacited by 5
  4. 2024ECLIPSE: Semantic Entropy-LCS for Cross-Lingual Industrial Log Parsingcited by 5
  5. 2024LogELECTRA: Self-supervised Anomaly Detection for Unstructured Logscited by 4
  6. 2025AdaptiveLog: An Adaptive Log Analysis Framework with the Collaboration of Large and Small Language Modelcited by 4
  7. 2025Mapping the Landscape of Generative AI in Network Monitoring and Managementcited by 4
  8. 2024Log Parsing using LLMs with Self-Generated In-Context Learning and Self-Correctioncited by 3
  9. 2023LogAI: A Library for Log Analytics and Intelligencecited by 2
  10. 2024HELP: Hierarchical Embeddings-based Log Parsingcited by 2
  11. 2024LUK: Empowering Log Understanding with Expert Knowledge from Large Language Modelscited by 2
  12. 2025System Log Parsing with Large Language Models: A Reviewcited by 2
  13. 2025Deep Learning-based Intrusion Detection Systems: A Surveycited by 2
  14. 2025SchemaCoder: Automatic Log Schema Extraction Coder with Residual Q-Tree Boostingcited by 2
  15. 2025Hypergraph-Guided Regex Filter Synthesis for Event-Based Anomaly Detectioncited by 2
  16. 2025AOI: Context-Aware Multi-Agent Operations via Dynamic Scheduling and Hierarchical Memory Compressioncited by 2
  17. 2022Leveraging Log Instructions in Log-based Anomaly Detectioncited by 1
  18. 2023Practical Anomaly Detection over Multivariate Monitoring Metrics for Online Servicescited by 1
  19. 2023Try with Simpler -- An Evaluation of Improved Principal Component Analysis in Log-based Anomaly Detectioncited by 1
  20. 2025ClusterRCA: An End-to-End Approach for Network Fault Localization and Classification for HPC Systemcited by 1
  21. 2025ALPHA: LLM-Enabled Active Learning for Human-Free Network Anomaly Detectioncited by 1
  22. 2025Task-Aware Reduction for Scalable LLM-Database Systemscited by 1
  23. 2026LogSieve: Task-Aware CI Log Reduction for Sustainable LLM-Based Analysiscited by 1
  24. 2022Failure Identification from Unstable Log Data using Deep Learningcited by 0
  25. 2023Interactive Log Parsing via Light-weight User Feedbackcited by 0
  26. 2023A Multi-Level, Multi-Scale Visual Analytics Approach to Assessment of Multifidelity HPC Systemscited by 0
  27. 2024Semi-supervised learning via DQN for log anomaly detectioncited by 0
  28. 2024MTAD: Tools and Benchmarks for Multivariate Time Series Anomaly Detectioncited by 0
  29. 2024What Information Contributes to Log-based Anomaly Detection? Insights from a Configurable Transformer-Based Approachcited by 0
  30. 2024TPLogAD: Unsupervised Log Anomaly Detection Based on Event Templates and Key Parameterscited by 0
  31. 2025AutoLogger: A Multi-Agent Framework for the End-to-End Automated Loggingcited by 0
  32. 2025MINES: Explainable Anomaly Detection through Web API Invariant Inferencecited by 0
  33. 2026MicLog: Towards Accurate and Efficient LLM-based Log Parsing via Progressive Meta In-Context Learningcited by 0
  34. 2026Testing Storage-System Correctness: Challenges, Fuzzing Limitations, and AI-Augmented Opportunitiescited by 0
  35. 2026SCOPE: Tree-based Self-Correcting Online Log Parsing via Syntactic-Semantic Collaborationcited by 0
  36. 2026LLM-Based Automated Diagnosis Of Integration Test Failures At Googlecited by 0
  37. 2026FAME: Failure-Aware Mixture-of-Experts for Message-Level Log Anomaly Detectioncited by 0
  38. 2026A Query Engine for the Agentscited by 0