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Tools and Benchmarks for Automated Log Parsing
Open accessAbstract
Logs are imperative in the development and maintenance process of many software systems. They record detailed runtime information that allows developers and support engineers to monitor their systems and dissect anomalous behaviors and errors. The increasing scale and complexity of modern software systems, however, make the volume of logs explodes. In many cases, the traditional way of manual log inspection becomes impractical. Many recent studies, as well as industrial tools, resort to powerful text search and machine learning-based analytics solutions. Due to the unstructured nature of logs, a first crucial step is to parse log messages into structured data for subsequent analysis. In recent years, automated log parsing has been widely studied in both academia and industry, producing a series of log parsers by different techniques. To better understand the characteristics of these log parsers, in this paper, we present a comprehensive evaluation study on automated log parsing and further release the tools and benchmarks for easy reuse. More specifically, we evaluate 13 log parsers on a total of 16 log datasets spanning distributed systems, supercomputers, operating systems, mobile systems, server applications, and standalone software. We report the benchmarking results in terms of accuracy, robustness, and efficiency, which are of practical importance when deploying automated log parsing in production. We also share the success stories and lessons learned in an industrial application at Huawei. We believe that our work could serve as the basis and provide valuable guidance to future research and deployment of automated log parsing.
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“While computationally lightweight, these methods struggle to adapt to evolving workloads and often suffer from high false-positive rates when applied to complex, real-world clusters.”
From LINEADAPTER: Parameter-Efficient Fine-Tuning for Log Anomaly Detection and Root Cause Analysis (2025) · cites this paper as [5]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
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“pretraining contamination can silently inflate benchmark performance, and contamination checks are now part of mainstream LLM evaluation practice [147], [44], [139].”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [147]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
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“LLM graders can help with structure and readability, but they are not a substitute for tool-grounded correctness and safety scoring.”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [147]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“This frequencyratio-based heuristic has been widely adopted in prior work on log anomaly detection, particularly in template blacklisting and rule construction [4], [5].”
From LINEADAPTER: Parameter-Efficient Fine-Tuning for Log Anomaly Detection and Root Cause Analysis (2025) · cites this paper as [5]
Experimental — accuracy not yet measured
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Cites
DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning
Du, M., Li, F., Zheng, G., & Srikumar, V. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3134015 · Cited by 1,835
Detecting large-scale system problems by mining console logs
Xu, W., Huang, L., Fox, A., Patterson, D., & Jordan, M. I. (2009) · Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles · doi:10.1145/1629575.1629587 · Cited by 1,332
Drain: an online log parsing approach with fixed depth tree
He, P., Zhu, J., Zheng, Z., & Lyu, M. R. (2017) · 2017 IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws.2017.13 · Cited by 943
Self-citationWhat Supercomputers Say: A Study of Five System Logs
Oliner, A. J., & Stearley, J. (2007) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn.2007.103 · Cited by 695
Experience Report: System Log Analysis for Anomaly Detection
He, S., Zhu, J., He, P., & Lyu, M. R.-T. (2016) · Proceedings/Proceedings - International Symposium on Software Reliability Engineering · doi:10.1109/issre.2016.21 · Cited by 675
Self-citationUsing magpie for request extraction and workload modelling
Barham, P., Donnelly, A., Isaacs, R., & Mortier, R. (2004) · Operating Systems Design and Implementation · Cited by 571
Execution Anomaly Detection in Distributed Systems through Unstructured Log Analysis
Fu, Q., Lou, J., Wang, Y., & Jiang, L. (2009) · Proceedings · doi:10.1109/icdm.2009.60 · Cited by 555
Log clustering based problem identification for online service systems
Lin, Q., Zhang, H., Lou, J., Zhang, Y., & Chen, X. (2016) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/2889160.2889232 · Cited by 493
A data clustering algorithm for mining patterns from event logs
Vaarandi, R. (2004) · IP Operations and Management · doi:10.1109/ipom.2003.1251233 · Cited by 480
Spell: Streaming Parsing of System Event Logs
Du, M., & Li, F. (2016) · 2016 IEEE 16th International Conference on Data Mining (ICDM) · doi:10.1109/icdm.2016.0103 · Cited by 423
Research works printed but not linked: 12
- 0, New automated log parsing
- 0, Logz io
- 0, Overview of logs-based metrics
- 0, Loghub A collection of system log datasets for intelligent log analysis
- 0, Automated root cause analysis for spark application failures
- 0, Log Management Service in the Cloud-Loggly
- 0, Log parsing - automated easy to use and efficient
- 0, Splunk
- 0, ElK
- 0, Logentries
- 0, A beginner? guide to logstash grok
- 0, Automated parsing log types
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Items 101–200 of 575
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Bhanage, D. A., Pawar, A., & Kotecha, K. V. (2021) · IEEE Access · doi:10.1109/access.2021.3128283 · Cited by 37
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Using large language models for template detection from security event logs
Vaarandi, R., & Bahşi, H. (2025) · International Journal of Information Security · doi:10.1007/s10207-025-01018-y · Cited by 26
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Ekelhart, A., Ekaputra, F. J., & Kiesling, E. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77385-4_38 · Cited by 25
LogSummary: Unstructured Log Summarization for Software Systems
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Zhang, L., Jia, T., Jia, M., Li, Y., Yang, Y., & Wu, Z. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671725 · Cited by 25
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Bui, H.-K., Lin, Y., Hwang, R., Lin, P., Nguyen, V.-L., & Lai, Y. (2021) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2021.103212 · Cited by 24
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Zhao, X., Jiang, Z., & Ma, J. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892726 · Cited by 23
LogReducer: Identify and Reduce Log Hotspots in Kernel on the Fly
Yu, G., Chen, P., Li, P., Weng, T., Zheng, H., Deng, Y., & Zheng, Z. (2023) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse48619.2023.00151 · Cited by 23
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Tao, S., Liu, Y., Meng, W., Ren, Z., Yang, H., Chen, X., Zhang, L., Xie, Y., Su, C., Oiao, X., et al. (2023) · International Workshop on Quality of Service · doi:10.1109/iwqos57198.2023.10188759 · Cited by 26
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Yang, L., Chen, J., Gao, S., Gong, Z., Zhang, H., Kang, Y., & Li, H. H. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3644386 · Cited by 26
AllInfoLog: Robust Diverse Anomalies Detection Based on All Log Features
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ClusterLog: Clustering Logs for Effective Log-based Anomaly Detection
Egersdoerfer, C., Zhang, D., & Dai, D. (2022) · IEEE/ACM Workshop on Fault Tolerance for HPC at eXtreme Scale (FTXS) · doi:10.1109/ftxs56515.2022.00006 · Cited by 27
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Zhang, S., Liu, Y., Meng, W., Bu, J., Yang, S., Sun, Y., Pei, D., Xu, J. J., Zhang, Y., Song, L., et al. (2020) · IEEE Access · doi:10.1109/access.2020.2972691 · Cited by 22
Xiao, R., Li, W., Lu, J., & Jin, S. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3400283 · Cited by 23
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Sakraoui, S., Ahmim, A., Derdour, M., Ahmim, M., Namane, S., & Dhaou, I. B. (2024) · IEEE Access · doi:10.1109/access.2024.3435920 · Cited by 25
No More Labelled Examples? An Unsupervised Log Parser with LLMs
Huang, J., Jiang, Z., Chen, Z., & Lyu, M. R.-T. (2025) · Proceedings of the ACM on software engineering. · doi:10.1145/3729377 · Cited by 33
Self-citationZhang, B., Zhang, H., Le, V.-H., Moscato, P., & Zhang, A. (2022) · Automated Software Engineering · doi:10.1007/s10515-022-00370-w · Cited by 23
Trustworthy AI-based Performance Diagnosis Systems for Cloud Applications: A Review
Xin, R., Wang, J., Chen, P., & Zhao, Z. (2025) · ACM Computing Surveys · doi:10.1145/3701740 · Cited by 27
An online log template extraction method based on hierarchical clustering
Yang, R., Qu, D., Qian, Y., Dai, Y., & Zhu, S. (2019) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-019-1430-4 · Cited by 21
Leveraging Large Language Models and BERT for Log Parsing and Anomaly Detection
Zhou, Y., Chen, Y., Rao, X., Zhou, Y., Li, Y., & Hu, C. (2024) · Mathematics · doi:10.3390/math12172758 · Cited by 22
Incident-aware Duplicate Ticket Aggregation for Cloud Systems
Liu, J., He, S., Chen, Z., Li, L., Kang, Y., Zhang, X., He, P., Zhang, H., Lin, Q., Xu, Z., et al. (2023) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse48619.2023.00193 · Cited by 23
Self-citationContext-Aware Learning for Anomaly Detection with Imbalanced Log Data
Sun, P., Yuepeng, E., Li, T., Wu, Y., Ge, J., You, J., & Wu, B. (2020) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-smartcity-dss50907.2020.00055 · Cited by 23
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Wang, P., Zhang, X., Chen, Y., & Cao, Z. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.104013 · Cited by 21
Monitoring Anomalous States of Computer Systems by Intellectual Analysis of Data of System Journals
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Log-Based Anomaly Detection with Multi-Head Scaled Dot-Product Attention Mechanism
Du, Q., Zhao, L. Y., Xu, J., Han, Y., & Zhang, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86472-9_31 · Cited by 19
Demonstration-Free: Towards More Practical Log Parsing with Large Language Models
Xiao, Y., Le, V.-H., & Zhang, H. (2024) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3691620.3694994 · Cited by 33
Self-supervised log parsing using semantic contribution difference
Yu, S., Chen, N., Wu, Y., & Dou, W. (2023) · Journal of Systems and Software · doi:10.1016/j.jss.2023.111646 · Cited by 21
Automatic Parsing and Utilization of System Log Features in Log Analysis: A Survey
Ma, J., Liu, Y., Wan, H., & Sun, G. (2023) · Applied Sciences · doi:10.3390/app13084930 · Cited by 23
LogGrep: Fast and Cheap Cloud Log Storage by Exploiting both Static and Runtime Patterns
Wei, J., Zhang, G., Chen, J., Wang, Y., Zheng, W., Sun, T., Wu, J., & Jiang, J. (2023) · European Conference on Computer Systems · doi:10.1145/3552326.3567484 · Cited by 18
OILog: An online incremental log keyword extraction approach based on MDP-LSTM neural network
Duan, X., Ying, S., Cheng, H., Yuan, W., & Yin, X. (2020) · Information Systems · doi:10.1016/j.is.2020.101618 · Cited by 18
Brandt, C., Panichella, A., Zaidman, A., & Beller, M. (2020) · International Conference on Mining Software Repositories (MSR) · doi:10.1145/3379597.3387485 · Cited by 17
LogOnline: A Semi-Supervised Log-Based Anomaly Detector Aided with Online Learning Mechanism
Wang, X., Song, J., Zhang, X., Tang, J., Gao, W., & Lin, Q. (2023) · 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase56229.2023.00043 · Cited by 18
Hue: A User-Adaptive Parser for Hybrid Logs
Xu, J., Fu, Q., Zhu, Z., Cheng, Y., Li, Z., Ma, Y., & He, P. (2023) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3611643.3616260 · Cited by 17
Self-citationNo, G., Lee, Y., Kang, H., & Kang, P. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108613 · Cited by 21
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Cheng, C. C.-C., Chen, S., Gong, N. Z., & Guan, Y. (2021) · Forensic Science International Digital Investigation · doi:10.1016/j.fsidi.2021.301193 · Cited by 18
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Khan, S., Parkinson, S., & Murphy, C. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120991 · Cited by 15
Pham, T.-A., & Lee, J. (2023) · IEEE Access · doi:10.1109/access.2023.3311146 · Cited by 15
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Rücker, N., & Maier, A. (2022) · Journal of Software Evolution and Process · doi:10.1002/smr.2426 · Cited by 15
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Gupta, P., Kumar, H., Kar, D., Bhukar, K., Aggarwal, P., & Mohapatra, P. (2023) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud60044.2023.00026 · Cited by 18
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Meng, W., Zaiter, F., Huang, Y., Liu, Y., Zhang, S., Zhang, Y., Zhu, Y., Zhang, T., Wang, E., Ren, Z., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.08938 · Cited by 15
Gholamian, S., & Ward, P. A. S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.12489 · Cited by 36
Volpert, S., Eichhammer, P., Held, F., Huffert, T., Reiser, H. P., & Domaschka, J. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.11.024 · Cited by 12
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Literature Survey on Log-Based Anomaly Detection Framework in Cloud
Meenakshi, Ramachandra, A. C., & Bhattacharya, S. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-2449-3_12 · Cited by 11
Pissanidis, D. L., & Demertzis, K. (2023) · Preprints.org · doi:10.20944/preprints202312.0205.v1 · Cited by 11
Detecting Anomalous Events on Distributed Systems Using Convolutional Neural Networks
Cheansunan, P., & Phunchongharn, P. (2019) · International Conference on Awareness Science and Technology (iCAST) · doi:10.1109/icawst.2019.8923357 · Cited by 10
Optimizing Logging and Monitoring in Heterogeneous Cloud Environments for IoT and Edge Applications
Kim, C., & Kim, S. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3304373 · Cited by 10
Few-Shot Log Anomaly Detection Based on Matching Networks
Han, C., Guan, B., Li, T., Kang, D., Qin, J., & Wu, Y. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3363626 · Cited by 13
LogETA: Time-aware cross-system log-based anomaly detection with inter-class boundary optimization
Gong, K., Luo, S., Pan, L., Zhang, L., Zhang, Y., & Yu, H. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.03.028 · Cited by 12
Zhou, J., Ying, S., Wang, S., Zhao, D., Xiang, J., Liang, K., & Liu, P. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3548050 · Cited by 13
Delog: A High-Performance Privacy Preserving Log Filtering Framework
Agrawal, A., Dixit, A., Shettar, N. A., Kapadia, D., Agrawal, V., Gupta, R. K., & Karlupia, R. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9006218 · Cited by 10
LEKG: A System for Constructing Knowledge Graphs from Log Extraction
Wang, F., Bundy, A., Li, X., Zhu, R., Nuamah, K., Xu, L., Mauceri, S., & Pan, J. Z. (2021) · International Joint Conference on Knowledge Graphs (IJCKG) · doi:10.1145/3502223.3502250 · Cited by 10
Log message anomaly detection with fuzzy C-means and MLP
Farzad, A., & Gulliver, T. A. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03300-1 · Cited by 10
LogLead - Fast and Integrated Log Loader, Enhancer, and Anomaly Detector
Mäntylä, M., Wang, Y., & Nyyssölä, J. (2024) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner60148.2024.00046 · Cited by 13
An Exploratory Study on How Non-Determinism in Large Language Models Affects Log Parsing
Astekin, M., Hort, M., & Moonen, L. (2024) · doi:10.1145/3643661.3643952 · Cited by 18
The Use of Template Miners and Encryption in Log Message Compression
Marjai, P., Lehotay–Kéry, P., & Kiss, A. (2021) · Computers · doi:10.3390/computers10070083 · Cited by 11
Large scale log anomaly detection via spatial pooling
Hirakawa, R., Uchida, H., Nakano, A., Tominaga, K., & Nakatoh, Y. (2021) · Cognitive Robotics · doi:10.1016/j.cogr.2021.10.001 · Cited by 11
Improving the Safety and Security of Software Systems by Mediating SAP Verification
Almufareh, M. F., & Humayun, M. (2023) · Applied Sciences · doi:10.3390/app13010647 · Cited by 10
Han, D., Sun, M., Li, M., & Chen, Q. (2023) · Applied Sciences · doi:10.3390/app13137668 · Cited by 9
FSMFLog: Discovering Anomalous Logs Combining Full Semantic Information and Multifeature Fusion
Niu, W., Li, Z., He, Z., Wang, A., Li, B., & Zhang, X. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3300690 · Cited by 9
A Comparative Study on Large Language Models for Log Parsing
Astekin, M., Hort, M., & Moonen, L. (2024) · Proceedings of the ... ACM-IEEE International Symposium on Empirical Software Engineering and Measurement/International Symposium on Empirical Software Engineering and Measurement · doi:10.1145/3674805.3686684 · Cited by 13
Metadata
- Authors
- Zhu, J., He, S., Liu, J., He, P., Xie, Q., Zheng, Z., & Lyu, M. R.
- Affiliations
- Huawei Technologies (China) (CN)
- Chinese University of Hong Kong (HK)
- Sun Yat-sen University (CN)
- ETH Zurich (CH)
- Southwest Minzu University (CN)
- Venue
- 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)
- Publisher
- IEEE
- Published
- 2019-05
- Pages
- 121-130
- OpenAlex
- W2963999143
- Cited by
- 574
- References
- 49
- Citations by year
- 2026: 57 · 2025: 86 · 2024: 108 · 2023: 92 · 2022: 62 · 2021: 89 · 2020: 65 · 2019: 15
- FWCI
- 49.91
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61332010, 61502401, 61722214)
- Chinese University of Hong Kong (14210717)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Keywords
- benchmarking, structured data extraction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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