The DOI matches its Crossref record; 48 of the 49 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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.
In the citing paper
“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.
In the citing paper
“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
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Cited by
Sorted by citations
Items 401–500 of 575
Visualization for Computer Program: A Survey
Zhang, W., Wen, Z., Pan, J., & Chen, W. (2023) · Journal of Computer-Aided Design & Computer Graphics · doi:10.3724/sp.j.1089.2023.19893 · Cited by 1
Empirical Analysis of LLMDPP: Advancing Log Parsing in the LLM Era
Wang, K., Zhang, S., Nan, H., Hou, X., Zou, J., & Miao, Z. (2025) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3711875.3736683 · Cited by 1
MFLog: Log Sequence Anomaly Detection via Multifeature Extraction
Cao, Z., Wang, P., Qiu, Y., Wang, X., Zhang, X., & Xu, W. (2025) · Software Testing Verification and Reliability · doi:10.1002/stvr.70010 · Cited by 1
CSLParser: A Collaborative Framework Using Small and Large Language Models for Log Parsing
Hong, W., Wu, Y., Zhang, L., Duan, C., Xiao, P., He, M., Yang, X., & Li, Y. (2025) · Proceedings/Proceedings - International Symposium on Software Reliability Engineering · doi:10.1109/issre66568.2025.00020 · Cited by 1
A multi-device Android mobile raw log Dataset for intelligent analysis
Bessa, J. A., Filho, R. M., Barreto, R., & de Freitas, R. (2025) · Brazilian Symposium on Computing System Engineering (SBESC) · doi:10.1109/sbesc68008.2025.11288820 · Cited by 1
GLU-PM: A Pretrained Model Toward Generalized Log Understanding
Liu, X., Zheng, S., Zou, H., Yu, H., & Gao, S. (2025) · IEEE Transactions on Reliability · doi:10.1109/tr.2025.3642425 · Cited by 1
Log-based anomaly detection using BiLSTM-Autoencoder
Bekkouche, M., Meski, M., Khodja, Y., Benslimane, S. M., & Tronci, E. (2025) · International Conference on Networking and Advanced Systems (ICNAS) · doi:10.1109/icnas68168.2025.11298040 · Cited by 1
RELog: Robust and Efficient Anomaly Detection Based on Complete Logs for Large-Scale Systems
Chai, X., Feng, X., Lou, Z., Sun, Y., & Guizani, M. (2026) · IEEE Transactions on Computers · doi:10.1109/tc.2026.3654206 · Cited by 1
Log Anomaly Detection via Transformers Pre-Trained on Massive Unlabeled Data
Yan, S., Shi, L., Ren, J., Wang, W., Sun, L., & Zhang, W. (2026) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2026.3654089 · Cited by 5
ErrorPrism: Reconstructing Error Propagation Paths in Cloud Service Systems
Pu, J., Li, Y., Chen, Z., Liu, J., Jiang, Z., Chen, J., Shi, R., Zheng, Z., & Zhang, T. (2025) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase63991.2025.00292 · Cited by 1
Self-citationLogExpertSolver: A Multi-Agent Framework for Domain-Specialized Log Parsing
Mao, C., Su, Y., & Li, D. (2025) · Asia-Pacific Software Engineering Conference (APSEC) · doi:10.1109/apsec66846.2025.00108 · Cited by 1
AI-Driven Optimization of Backend and Cloud Infrastructure in Large-Scale Financial Systems
Mishra, V. K. (2026) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-31286 · Cited by 2
A GNN-Based Log Anomaly Detection Framework with Prompt Learning for Edge Computing
Hu, X., Feng, G., Huang, X., Kong, X., & Lv, H. (2026) · Computers · doi:10.3390/computers15050273 · Cited by 1
Small Is Beautiful: A Practical and Efficient Log Parsing Framework
Wang, M., & Huo, Y. (2026) · Proceedings of the ACM on software engineering. · doi:10.1145/3797145 · Cited by 1
Masri, Y., Ma, E., Wang, Z., Rogers, J., & Yang, C. P. (2026) · Computers · doi:10.3390/computers15070430 · Cited by 4
NovaADLS: Semi-supervised Anomaly Detection in Log Series
Roqaya, N., Jabeen, H., & Papenbrock, T. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-32726-0_14 · Cited by 1
Network Security Systems Log Analysis for Trends and Insights: A Case Study
Meena, A. K., Hubballi, N., Singh, Y., Bhatia, V., & Franke, K. (2020) · IEEE International Conference on Advanced Networks and Telecommunications Systems · doi:10.1109/ants50601.2020.9342776 · Cited by 1
LogCAD: An Efficient and Robust Model for Log-Based Conformal Anomaly Detection
Liu, C., Liang, M., Hou, J., Gu, Z., & Wang, Z. (2022) · Security and Communication Networks · doi:10.1155/2022/5822124 · Cited by 1
PLQ: An Efficient Approach to Processing Pattern-Based Log Queries
Chen, J., Wang, P., Qiao, F., Du, S.-Q., & Wang, W. (2022) · Journal of Computer Science and Technology · doi:10.1007/s11390-020-0653-5 · Cited by 1
Jayapal, C., Gokul, S., Kevin Samuel, C., & Joshua, A. (2023) · International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA) · doi:10.1109/icaeca56562.2023.10199556 · Cited by 1
Comparative Analysis of Pattern Mining Algorithms for Event Logs
Gasimov, O., Vaarandi, R., & Pihelgas, M. (2023) · IEEE International Conference on Cyber Security and Resilience (CSR) · doi:10.1109/csr57506.2023.10224996 · Cited by 1
Chen, P., Chao, G., Li, Y., He, H., Lei, H., Li, M., Gao, D., & Guo, S. (2023) · IEEE International Conference on Image Processing and Computer Applications (ICIPCA) · doi:10.1109/icipca59209.2023.10257676 · Cited by 1
Chitnis, P., & Asthana, A. (2023) · IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) · doi:10.1109/issrew60843.2023.00064 · Cited by 1
Macková, K., Benk, D., & Šrotýř, M. (2023) · International Conference on Information and Communication Systems (ICICS) · doi:10.1109/icics60529.2023.10330520 · Cited by 1
Cognition: Accurate and Consistent Linear Log Parsing Using Template Correction
Tian, R., Diao, Z., Jiang, H., & Xie, G. (2023) · Journal of Computer Science and Technology · doi:10.1007/s11390-021-1691-3 · Cited by 2
Log Anomaly Detection Based on Semantic Features and Topic Features
Wang, P., Zhang, X., & Cao, Z. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-0808-6_24 · Cited by 1
A Distributed Storage System for System Logs Based on Hybrid Compression Scheme
Chang, B., Zhou, F., Wang, Z., Wen, Y., & Zhang, B. (2023) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/ispa-bdcloud-socialcom-sustaincom59178.2023.00127 · Cited by 1
Unsupervised Microservice Log Anomaly Detection Method Based on Graph Neural Network
Liang, X., Li, L., & Peng, H. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-7184-4_17 · Cited by 2
Decoding Logs for Automatic Metric Identification
Gupta, P., Mohapatra, P., Kar, D., Nagar, S., Ahn, J., Paradkar, A. M., & Srivatsa, M. (2024) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud62652.2024.00045 · Cited by 1
TurboLog: A Turbocharged Lossless Compression Method for System Logs via Transformer
Chang, B., Wang, Z., Li, S., Zhou, F., Yu, W., & Zhang, B. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10649957 · Cited by 1
A Review of Software Testing Process Log Parsing and Mining
Xue, K., Han, Q., Sheng, H., Shi, Z., & Qiao, Y. (2024) · IEEE International Conference on Software Services Engineering (SSE) · doi:10.1109/sse62657.2024.00055 · Cited by 1
Enhancing IoT Security: A Novel Attack Detection Model for IoT Logs with Machine Learning Techniques
Jacob, A. V., Issac, E., & krishna, A. A. (2024) · International Conference on Smart Computing and Communications (ICSCC) · doi:10.1109/icscc62041.2024.10690532 · Cited by 1
Improving log anomaly detection via spatial pooling: Combining SPClassifier with ensemble method
Uchida, H., Tominaga, K., Itai, H., Li, Y., & Nakatoh, Y. (2024) · Cognitive Robotics · doi:10.1016/j.cogr.2024.10.001 · Cited by 1
Multi-Channel Fusion Model for Data Logs Analysis and Anomaly Detection in Data Centers
Abubaker, I., Owda, M., & Owda, A. Y. (2024) · International Conference of Science and Information Technology in Smart Administration (ICSINTESA) · doi:10.1109/icsintesa62455.2024.10748224 · Cited by 1
VCRLog: Variable Contents Relationship Perception for Log-based Anomaly Detection
Wang, J., Li, T., Zhang, R., Tang, Z., Wu, D., & Yang, Z. (2024) · Proceedings/Proceedings - International Symposium on Software Reliability Engineering · doi:10.1109/issre62328.2024.00025 · Cited by 1
Console Log Explainer: A Framework for Generating Automated Explanations using LLM
Shanto, S. S., Paul, R., Ahmed, Z., Reza, A. S., Islam, K. M., & Roy, S. S. (2024) · International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings) · doi:10.1109/aibthings63359.2024.10863559 · Cited by 1
Developing a Taxonomy for Advanced Log Parsing Techniques
Sedki, I., Hamou‐Lhadj, A., Mohamed, O. A., & Ezzati‐Jivan, N. (2025) · IEEE International Conference on Program Comprehension (ICPC) · doi:10.1109/icpc66645.2025.00061 · Cited by 1
E-Log: Fine-Grained Elastic Log-Based Anomaly Detection and Diagnosis for Databases
Zhang, L., Jia, T., Tan, X., Huang, X., Jia, M., Liu, H., Wu, Z., & Li, Y. (2025) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2025.3594870 · Cited by 4
Tengu: A Distributed Audit Log Storage System
Bi, P., Zang, T., & Yun, X. (2025) · International Symposium on Big Data and Applied Statistics (ISBDAS) · doi:10.1109/isbdas64762.2025.11117014 · Cited by 1
Model generation of component-based systems
Salva, S., & Blot, E. (2020) · Software Quality Journal · doi:10.1007/s11219-019-09485-y · Cited by 1
Coordinated Analysis of Heterogeneous Monitor Data in Enterprise Clouds for Incident Response
Thakore, U., Ramasamy, H. V., & Sanders, W. H. (2019) · IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) · doi:10.1109/issrew.2019.00042 · Cited by 1
Thakore, U., Ranchal, R., Wei, Y.-H., & Ramasamy, H. V. (2019) · IEEE Symposium on Reliable Distributed Systems (SRDS) · doi:10.1109/srds47363.2019.00039 · Cited by 1
A Comparative Evaluation of Big Data Frameworks for Log Processing.
Boros, A. P., Lehotay–Kéry, P., & Kiss, A. (2020) · Cited by 1
Automated error log resolution
Fuller, M. A., Brighton, E., Schiewe, M., Das, D., Cerny, T., & Tisnovsky, P. (2021) · ACM Symposium on Applied Computing · doi:10.1145/3412841.3442004 · Cited by 1
An Efficient Analytical Approach to Visualize Text-Based Event Logs for Semiconductor Equipment
Lee, G., & Jeong, J. (2021) · Applied Sciences · doi:10.3390/app11135944 · Cited by 1
Parallel Version of the Framework for Clustering Error Messages
Vorobyov, M., Zhukov, K., Grigorieva, M., & Korobkov, S. (2021) · Lobachevskii Journal of Mathematics · doi:10.1134/s1995080221070246 · Cited by 1
A Heuristic Approach Using Template Miners for Error Prediction in Telecommunication Networks
Marjai, P., Lehotay–Kéry, P., & Kiss, A. (2022) · IEEE Access · doi:10.1109/access.2022.3221427 · Cited by 2
Effective Removal of Operational Log Messages: an Application to Model InferencePreprint
Shin, D., Bianculli, D., & Briand, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.07194 · Cited by 1
Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly DetectionPreprint
Wang, Z., Chen, Z., Ni, J., Liu, H., Chen, H., & Tang, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.13361 · Cited by 1
Forensic investigation of event logs by automatic anomaly detection
Studiawan, H. (2020) · Murdoch University Research Portal · Cited by 1
Automated Evolution of Feature Logging Statement Levels Using Git Histories and Degree of InterestPreprint
Tang, Y., Spektor, A., Khatchadourian, R., & Bagherzadeh, M. (2020) · arXiv (Cornell University) · doi:10.5281/zenodo.3698983 · Cited by 1
Deep learning based log anomaly detection with time differences
Sağında, B. (2020) · Cited by 1
AWSOM-LP: An Effective Log Parsing Technique Using Pattern Recognition and Frequency AnalysisPreprint
Sedki, I., Hamou‐Lhadj, A., & Mohamed, O. A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.15473 · Cited by 3
Peer Review #1 of "Log-based software monitoring: a systematic mapping study (v0.1)"
(2021) · PeerJ Computer Science · doi:10.7287/peerj-cs.489v0.1/reviews/1 · Cited by 1
LINEADAPTER: Parameter-Efficient Fine-Tuning for Log Anomaly Detection and Root Cause Analysis
Liu, X., Bao, W., Zhang, Y., Li, Y., Ranjan, R., & Jha, D. N. (2025) · 2025 IEEE 31th International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads67057.2025.11322907
Delog: A Privacy Preserving Log Filtering Framework for Online Compute PlatformsPreprint
Agrawal, A., Dixit, A., Shettar, N. A., Kapadia, D., Karlupia, R., Agrawal, V., & Gupta, R. K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.04843
Xie, X., Wang, Z., Xiao, X., Yang, L., Huang, S., & Li, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.02765 · Cited by 3
Two Techniques For Automated Logging Statement Evolution
Spektor, A. (2020) · CUNY Academic Works (City University of New York)
Building an Adaptive Logs Classification System: Industrial Report
Rudakov, K., Novikov, A., Sitnikov, A., Tsymbalov, E., & Zverev, A. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-71472-7_15
Peer Review #2 of "Log-based software monitoring: a systematic mapping study (v0.1)"
(2021) · PeerJ Computer Science · doi:10.7287/peerj-cs.489v0.1/reviews/2
Peer Review #2 of "Log-based software monitoring: a systematic mapping study (v0.2)"
(2021) · PeerJ Computer Science · doi:10.7287/peerj-cs.489v0.2/reviews/2
Tejaswini, S., & Nasreen, A. (2021) · International Journal of Engineering and Advanced Technology · doi:10.35940/ijeat.e2816.0610521
Code quality improvement for Intel Windows Graphics Driver
Parveen, R., & Varma, N. S. (2021) · Global Transitions Proceedings · doi:10.1016/j.gltp.2021.08.011
Reverse Engineering Models of Concurrent Communicating Systems From Event LogsPreprint
Salva, S. (2021)
Correlating Time Series Signals and Event Logs in Embedded Systems
Krosman, K., & Sosnowski, J. S. (2021) · Sensors · doi:10.3390/s21217128
Peer Review #1 of "Log-based software monitoring: a systematic mapping study (v0.2)"
(2021) · PeerJ Computer Science · doi:10.7287/peerj-cs.489v0.2/reviews/1
Learning of behavioural models and dependency graphs for communicating systems with CkTailv2
Salva, S., & Blot, E. (2022) · International Journal on Software Tools for Technology Transfer · doi:10.1007/s10009-022-00651-5
Network anomaly detection based on keyword embedding log
Song, Y. I., Yan, Z., Qin, Y., Xie, Y., Ye, X., & Ouyang, Y. (2022) · ITU Journal on Future and Evolving Technologies · doi:10.52953/icxq5497
Root Cause Analysis And Improvement In Windows System Based On Windows Performance Toolkit WPT
Assi, M. J., Fahad, A. A., & Al-Sarray, B. (2022) · Iraqi Journal of Science · doi:10.24996/ijs.2022.63.11.39
Analysis Of the Behavior of Cyberattacks on Online Services Using the Cyber Threat Classification
Владиславович, И. С., & Дмитриевич, К. Д. (2022) · Journal of Systems Engineering and Information Technology (JOSEIT) · doi:10.29207/joseit.v1i2.4944
Bottleneck identification and failure prevention with procedural learning in 5G RAN
Sundqvist, T., Bhuyan, M. H., & Elmroth, E. (2023) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid57682.2023.00047
Revolutionizing Log Parsing for Modern Software Systems
Petrescu, S. (2023) · IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) · doi:10.1109/issrew60843.2023.00036
Privacy-Centric Log Parsing for Timely, Proactive Personal Data Protection
Sedki, I. (2023) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3611643.3617847
Smart Log Analyzer for Anomaly Detection in Distributed Systems
Journal, I. (2024) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem28072
Rahman, R. U. (2023) · Sigma Journal of Engineering and Natural Sciences – Sigma Mühendislik ve Fen Bilimleri Dergisi · doi:10.14744/sigma.2024.00004
Non-Deterministic and Semi-Supervised Log-based Anomaly Detection
Kumar, S., Murgai, V., Bashir, Z., & Singh, S. (2023) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshps58843.2023.10464781
Li, L. (2023) · Highlights in Business Economics and Management · doi:10.54097/hbem.v21i.14946
A log parsing method based on non-fixed search tree
Xu, Y., Wang, Y., & Guan, R. (2024) · International Conference on Neural Networks, Information and Communication Engineering (NNICE) · doi:10.1109/nnice61279.2024.10498742
An Automated and Verbose Approach for Detecting Anomalies in Cloud Computing Platform Using Logs
Vervaet, A., Chabchoub, Y., Callau-Zori, M., & Chiky, R. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-70259-4_7
Addressing a Malicious Tampering Attack on the Default Isolation Level in DBMS
Alhajri, A., & Jhumka, A. (2023) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom60117.2023.00371
AML: An accuracy metric model for effective evaluation of log parsing techniques
Sedki, I., Hamou‐Lhadj, A., & Mohamed, O. A. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2024.112154 · Cited by 1
Productionizing PILAR as a Logstash Plugin
Abraham, A., Dani, Y., & Zhang, K. (2024) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3663529.3664460
A Preliminary Study on the Privacy Concerns of Using IP Addresses in Log Data
Sedki, I. (2024) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3663529.3663791
Local Search-based Approach for Cost-effective Job Assignment on Large Language Models
Liu, Y., Zhang, H., Le, V.-H., Miao, Y., & Li, Z. (2024) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3638530.3654104
Graph classification using high-difference-frequency subgraph embedding
Gao, T., & Xu, Y. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128369
IPLog: An Efficient Log Parsing Method Based on Few-Shot Learning
Liu, S., Yun, L., Nie, S., Zhang, G., & Li, W. (2024) · Electronics · doi:10.3390/electronics13163324 · Cited by 1
In Industrial Embedded Software, are Some Compilation Errors Easier to Localize and Fix than Others?
Han, F., Eldh, S., Wiklund, K., Ermedah, A., Haller, P., & Artho, C. V. (2024) · IEEE International Conference on Software Testing, Verification and Validation (ICST) · doi:10.1109/icst60714.2024.00042 · Cited by 1
Semantic-Aware Log Understanding and Analysis
Huang, S., & Luan, Z. (2024) · ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC) · doi:10.1145/3625549.3658830
PDFA Distillation with Error Bound Guarantees
Baumgartner, R., & Verwer, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-71112-1_4
CPLS: Optimizing the Assignment of LLM Queries
Liu, Y., Zhang, H., Li, Z., & Miao, Y. (2024) · IEEE International Conference on Software Maintenance (ICSM) · doi:10.1109/icsme58944.2024.00024
A Test Case Generation Scheduling Model Based on Running Event Interval Characteristics
Li, Y., Qin, T., Zou, J., Li, H., Yan, C.-B., & Guan, X. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3520311
LogNotion: Highlighting Massive Logs to Assist Human Reading and Decision Making
Chu, G., Wang, J., Sun, T., Qi, Q., Sun, H., Zhuang, Z., & Liao, J. (2025) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2025.3528327
A Log Parsing Algorithm with Source Code
Wu, D., & Zhou, H. (2024) · IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT) · doi:10.1109/iccasit62299.2024.10828050
Hierarchical Prompt Tuning for System-Incremental Log Analysis
Zhou, M., Yang, W., Ma, L., Jiang, S., Xu, B., & Xiao, Y. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890029
Ban, T., Ndichu, S., Yamada, A., Ozawa, S., Takahashi, T., & Inoue, D. (2025) · doi:10.36227/techrxiv.174286318.89102383/v2
Log Anomaly Detection Based on StackLog
徐, 克. (2025) · Computer Science and Application · doi:10.12677/csa.2025.154111
Li, Z., Jiang, Y., Li, H., & Wang, Y. (2024) · International Conference on Digital Society and Intelligent Systems (DSInS) · doi:10.1109/dsins64146.2024.10992236
Design and Implementation of Online Log Parsing Method Based on ALBERT and K-Means ++ Clustering
Hao, R., Lin, M., Wang, Y., Zhou, Z., & Zeng, S. (2025) · International Conference on Software Engineering and Computer Science (CSECS) · doi:10.1109/csecs64665.2025.11009471
SGMNet: A Supervised Seeded Graph-Matching Method for Cyber Threat Hunting
Zhang, C. C., & Su, L. (2025) · Symmetry · doi:10.3390/sym17060898
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 ›
Are you an author, or do you see a problem with this page? Request a review
