# A Survey on Automated Log Analysis for Reliability Engineering

arXiv: https://arxiv.org/abs/2009.07237 · HTML: https://mlsanity.com/paper/2009.07237/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2020.

## Built on by (most cited first, 30 of 50)

- [Log-based Anomaly Detection with Deep Learning: How Far Are We?](https://mlsanity.com/paper/2202.04301/) (2022)
- [Experience Report: Deep Learning-based System Log Analysis for Anomaly Detection](https://mlsanity.com/paper/2107.05908/) (2021)
- [LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection](https://mlsanity.com/paper/2309.01189/) (2023)
- [LLMParser: An Exploratory Study on Using Large Language Models for Log Parsing](https://mlsanity.com/paper/2404.18001/) (2024)
- [Face It Yourselves: An LLM-Based Two-Stage Strategy to Localize Configuration Errors via Logs](https://mlsanity.com/paper/2404.00640/) (2024)
- [Lemur: Log Parsing with Entropy Sampling and Chain-of-Thought Merging](https://mlsanity.com/paper/2402.18205/) (2024)
- [AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges](https://mlsanity.com/paper/2304.04661/) (2023)
- [UniLog: Deploy One Model and Specialize it for All Log Analysis Tasks](https://mlsanity.com/paper/2112.03159/) (2021)
- [Heterogeneous Anomaly Detection for Software Systems via Semi-supervised Cross-modal Attention](https://mlsanity.com/paper/2302.06914/) (2023)
- [Large Language Models for Security Operations Centers: A Comprehensive Survey](https://mlsanity.com/paper/2509.10858/) (2025)
- [A Comprehensive Survey of Logging in Software: From Logging Statements Automation to Log Mining and Analysis](https://mlsanity.com/paper/2110.12489/) (2021)
- [On the Effectiveness of Log Representation for Log-based Anomaly Detection](https://mlsanity.com/paper/2308.08736/) (2023)
- [Learning Representations on Logs for AIOps](https://mlsanity.com/paper/2308.11526/) (2023)
- [Demystifying and Extracting Fault-indicating Information from Logs for Failure Diagnosis](https://mlsanity.com/paper/2409.13561/) (2024)
- [A Taxonomy of Anomalies in Log Data](https://mlsanity.com/paper/2111.13462/) (2021)
- [ECLIPSE: Semantic Entropy-LCS for Cross-Lingual Industrial Log Parsing](https://mlsanity.com/paper/2405.13548/) (2024)
- [LogELECTRA: Self-supervised Anomaly Detection for Unstructured Logs](https://mlsanity.com/paper/2402.10397/) (2024)
- [Log Parsing using LLMs with Self-Generated In-Context Learning and Self-Correction](https://mlsanity.com/paper/2406.03376/) (2024)
- [AdaptiveLog: An Adaptive Log Analysis Framework with the Collaboration of Large and Small Language Model](https://mlsanity.com/paper/2501.11031/) (2025)
- [Mapping the Landscape of Generative AI in Network Monitoring and Management](https://mlsanity.com/paper/2502.08576/) (2025)
- [System Log Parsing with Large Language Models: A Review](https://mlsanity.com/paper/2504.04877/) (2025)
- [Hypergraph-Guided Regex Filter Synthesis for Event-Based Anomaly Detection](https://mlsanity.com/paper/2509.06911/) (2025)
- [LogAI: A Library for Log Analytics and Intelligence](https://mlsanity.com/paper/2301.13415/) (2023)
- [HELP: Hierarchical Embeddings-based Log Parsing](https://mlsanity.com/paper/2408.08300/) (2024)
- [LUK: Empowering Log Understanding with Expert Knowledge from Large Language Models](https://mlsanity.com/paper/2409.01909/) (2024)
- [Deep Learning-based Intrusion Detection Systems: A Survey](https://mlsanity.com/paper/2504.07839/) (2025)
- [SchemaCoder: Automatic Log Schema Extraction Coder with Residual Q-Tree Boosting](https://mlsanity.com/paper/2508.18554/) (2025)
- [AOI: Context-Aware Multi-Agent Operations via Dynamic Scheduling and Hierarchical Memory Compression](https://mlsanity.com/paper/2512.13956/) (2025)
- [Leveraging Log Instructions in Log-based Anomaly Detection](https://mlsanity.com/paper/2207.03206/) (2022)
- [Practical Anomaly Detection over Multivariate Monitoring Metrics for Online Services](https://mlsanity.com/paper/2308.09937/) (2023)

## 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.