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Logs generated by large-scale software systems provide crucial information for engineers to understand the system status and diagnose problems of the systems.
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P. He, J. Zhu, S. He, J. Li, and M. R. Lyu, “An evaluation study on log parsing and its use in log mining,” in 2016 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) . IEEE, 2016, pp. 654–661
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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
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2017
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2017
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A. Radford, K. Narasimhan, T. Salimans, and I. Sutskever, “Improving language understanding by generative pre-training,” 2018
2018
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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 ESEC/FSE 2019 , 2019, pp. 807–817
2019
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J. Liu, J. Zhu, S. He, P. He, Z. Zheng, and M. R. Lyu, “Logzip: extracting hidden structures via iterative clustering for log compression,” in 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2019, pp. 863–873
2019
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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
2019
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“A large collection of system log datasets for ai-powered log analytics,” 2021. [Online]. Available: https://github.com/logpai/loghub
2021
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L. Cui, Y. Wu, J. Liu, S. Yang, and Y. Zhang, “Template-based named entity recognition using bart,” in Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 , 2021, pp. 1835–1845
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K. Hambardzumyan, H. Khachatrian, and J. May, “Warp: Word-level adversarial reprogramming,” 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. 4921–4933
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2021
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2019
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J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) , 2019, pp. 4171–4186
2019
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A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever, “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
2019
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I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” in 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019 . OpenReview.net, 2019. [Online]. Available: https://openreview.net/forum?id=Bkg6RiCqY7
2019
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Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. R. Salakhutdinov, and Q. V. Le, “Xlnet: Generalized autoregressive pretraining for language understanding,” Advances in neural information processing systems , vol. 32, 2019
2019
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B. Zhang, H. Zhang, P. Moscato, and A. Zhang, “Anomaly detection via mining numerical workflow relations from logs,” in 2020 International Symposium on Reliable Distributed Systems (SRDS) . IEEE, 2020, pp. 195–204
2020
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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
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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
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Later among the works it cites.
F. Setianto, E. Tsani, F. Sadiq, G. Domalis, D. Tsakalidis, and P. Kostakos, “Gpt-2c: a parser for honeypot logs using large pre-trained language models,” in Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining , 2021, pp. 649–653
2021
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2021
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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
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“HDFS dataset,” 2022. [Online]. Available: https://github.com/logpai/loghub/tree/master/HDFS
2022
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“BGL dataset,” 2022. [Online]. Available: https://github.com/logpai/loghub/tree/master/BGL
2022
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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
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C. Wang, Y. Yang, C. Gao, Y. Peng, H. Zhang, and M. R. Lyu, “No more fine-tuning? an experimental evaluation of prompt tuning in code intelligence,” in Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2022, pp. 382–394
2022
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X. Han, W. Zhao, N. Ding, Z. Liu, and M. Sun, “Ptr: Prompt tuning with rules for text classification,” AI Open , vol. 3, pp. 182–192, 2022
2022
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R. Ma, X. Zhou, T. Gui, Y. Tan, L. Li, Q. Zhang, and X. Huang, “Template-free prompt tuning for few-shot NER,” in Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . Seattle, United States: Association for Computational Linguistics, Jul. 2022, pp. 5721–5732. [Online]. Available: https://aclanthology.org/2022.naacl-main.420
2022
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2022
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“A toolkit for automated log parsing,” 2022. [Online]. Available: https://github.com/logpai/logparser
2022
Later among the works it cites.
“Artifact for ”guidelines for assessing the accuracy of log message template identification techniques”,” 2022. [Online]. Available: https://doi.org/10.6084/m9.figshare.18858332
2022
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X. Li, P. Chen, L. Jing, Z. He, and G. Yu, “Swisslog: Robust anomaly detection and localization for interleaved unstructured logs,” IEEE Transactions on Dependable and Secure Computing , 2022
2022
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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
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B. Zhang, H. Zhang, V.-H. Le, P. Moscato, and A. Zhang, “Semi-supervised and unsupervised anomaly detection by mining numerical workflow relations from system logs,” Automated Software Engineering , vol. 30, no. 1, p. 4, 2023
2023
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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.