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Software logs play an essential role in ensuring the reliability and maintainability of large-scale software systems, as they are often the sole source of runtime information.
Y. Liu, X. Zhang, S. He, H. Zhang, L. Li, Y. Kang, Y. Xu, M. Ma, Q. Lin, Y. Dang et al. , “Uniparser: A unified log parser for heterogeneous log data,” in Proceedings of the ACM Web Conference 2022 , 2022, pp. 1893–1901
1901
Earlier work this paper cites.
R. Vaarandi, “A data clustering algorithm for mining patterns from event logs,” in Proceedings of the 3rd IEEE Workshop on IP Operations & Management (IPOM 2003)(IEEE Cat. No. 03EX764) . Ieee, 2003, pp. 119–126
2003
Earlier work this paper cites.
Z. M. Jiang, A. E. Hassan, P. Flora, and G. Hamann, “Abstracting execution logs to execution events for enterprise applications (short paper),” in 2008 The Eighth International Conference on Quality Software . IEEE, 2008, pp. 181–186
2008
Earlier work this paper cites.
Q. Fu, J.-G. Lou, Y. Wang, and J. Li, “Execution anomaly detection in distributed systems through unstructured log analysis,” in 2009 Ninth IEEE International Conference on Data Mining . IEEE, 2009, pp. 149–158
2009
Earlier work this paper cites.
M. Nagappan and M. A. Vouk, “Abstracting log lines to log event types for mining software system logs,” in 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010) . IEEE, 2010, pp. 114–117
2010
Earlier work this paper cites.
L. Tang, T. Li, and C.-S. Perng, “Logsig: Generating system events from raw textual logs,” in Proceedings of the 20th ACM international conference on Information and knowledge management , 2011, pp. 785–794
2011
Earlier work this paper cites.
B. Russo, G. Succi, and W. Pedrycz, “Mining system logs to learn error predictors: a case study of a telemetry system,” Empirical Software Engineering , vol. 20, pp. 879–927, 2015
2015
Earlier work this paper cites.
N. Gurumdimma, A. Jhumka, M. Liakata, E. Chuah, and J. Browne, “Crude: combining resource usage data and error logs for accurate error detection in large-scale distributed systems,” in 2016 IEEE 35th Symposium on Reliable Distributed Systems (SRDS) . IEEE, 2016, pp. 51–60
2016
Earlier work this paper cites.
M. Du and F. Li, “Spell: Streaming parsing of system event logs,” in 2016 IEEE 16th International Conference on Data Mining (ICDM) . IEEE, 2016, pp. 859–864
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
S. He, J. Zhu, P. He, and M. R. Lyu, “Experience report: System log analysis for anomaly detection,” in 2016 IEEE 27th international symposium on software reliability engineering (ISSRE) . IEEE, 2016, pp. 207–218
2016
Earlier work this paper cites.
M. Du, F. Li, G. Zheng, and V. Srikumar, “Deeplog: Anomaly detection and diagnosis from system logs through deep learning,” in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security , 2017, pp. 1285–1298
2017
Earlier work this paper cites.
S. Lu, B. Rao, X. Wei, B. Tak, L. Wang, and L. Wang, “Log-based abnormal task detection and root cause analysis for spark,” in 2017 IEEE International Conference on Web Services (ICWS) . IEEE, 2017, pp. 389–396
2017
Earlier work this paper cites.
P. He, J. Zhu, Z. Zheng, and M. R. Lyu, “Drain: An online log parsing approach with fixed depth tree,” in 2017 IEEE International Conference on Web Services (ICWS) . IEEE, 2017, pp. 33–40
2017
Cited alongside, same era.
A. Das, F. Mueller, C. Siegel, and A. Vishnu, “Desh: deep learning for system health prediction of lead times to failure in hpc,” in Proceedings of the 27th International Symposium on High-Performance Parallel and Distributed Computing , 2018, pp. 40–51
2018
Cited alongside, same era.
X. Zhang, Y. Xu, Q. Lin, B. Qiao, H. Zhang, Y. Dang, C. Xie, X. Yang, Q. Cheng, Z. Li et al. , “Robust log-based anomaly detection on unstable log data,” in Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2019, pp. 807–817
2019
Cited alongside, same era.
J. Zhu, S. He, J. Liu, P. He, Q. Xie, Z. Zheng, and M. R. Lyu, “Tools and benchmarks for automated log parsing,” in 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) . IEEE, 2019, pp. 121–130
Z. A. Khan, D. Shin, D. Bianculli, and L. Briand, “Guidelines for assessing the accuracy of log message template identification techniques,” in Proceedings of the 44th International Conference on Software Engineering (ICSE’22) . ACM, 2022
2022
Later among the works it cites.
“Artifact for ”guidelines for assessing the accuracy of log message template identification techniques”,” 2023. [Online]. Available: https://doi.org/10.6084/m9.figshare.18858332
2023
Closest in time.
V.-H. Le and H. Zhang, “Log parsing with prompt-based few-shot learning,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) , 2023, pp. 2438–2449
2023
Closest in time.
“Openai chatgpt,” 2023. [Online]. Available: https://openai.com/blog/chatgpt/
2023
Closest in time.
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2019
Cited alongside, same era.
H. Dai, H. Li, C. S. Chen, W. Shang, and T.-H. Chen, “Logram: Efficient log parsing using n-gram dictionaries,” IEEE Transactions on Software Engineering , 2020
2020
Cited alongside, same era.
X. Li, P. Chen, L. Jing, Z. He, and G. Yu, “Swisslog: Robust and unified deep learning based log anomaly detection for diverse faults,” in 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) . IEEE, 2020, pp. 92–103
2020
Cited alongside, same era.
2020
Cited alongside, same era.
S. Nedelkoski, J. Bogatinovski, A. Acker, J. Cardoso, and O. Kao, “Self-supervised log parsing,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases . Springer, 2020, pp. 122–138
2020
Cited alongside, same era.
V.-H. Le and H. Zhang, “Log-based anomaly detection without log parsing,” in 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE) , 2021, pp. 492–504
2021
Cited alongside, same era.
X. L. Li and P. Liang, “Prefix-tuning: Optimizing continuous prompts for generation,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) , 2021, pp. 4582–4597
2021
Cited alongside, same era.
V.-H. Le and H. Zhang, “Log-based anomaly detection with deep learning: How far are we?” in Proceedings of the 44th International Conference on Software Engineering , 2022, pp. 1356–1367
2022
Cited alongside, same era.
S. Tao, W. Meng, Y. Cheng, Y. Zhu, Y. Liu, C. Du, T. Han, Y. Zhao, X. Wang, and H. Yang, “Logstamp: Automatic online log parsing based on sequence labelling,” ACM SIGMETRICS Performance Evaluation Review , vol. 49, no. 4, pp. 93–98, 2022
2022
Cited alongside, same era.
2023
Closest in time.
2023
Closest in time.
Z. Li, C. Luo, T.-H. Chen, W. Shang, S. He, Q. Lin, and D. Zhang, “Did we miss something important? studying and exploring variable-aware log abstraction,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) , 2023, pp. 830–842
2023
Closest in time.
“Gpt-3.5-turbo,” 2023. [Online]. Available: https://platform.openai.com/docs/models/gpt-3-5
2023
Closest in time.
“A toolkit for automated log parsing,” 2023. [Online]. Available: https://github.com/logpai/logparser
2023
Closest in time.
P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig, “Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing,” ACM Computing Surveys , vol. 55, no. 9, pp. 1–35, 2023
2023
Closest in time.
2023
Closest in time.