| Experience report: system log analysis for anomaly detection S He, J Zhu, P He, MR Lyu ISSRE 2016, 207-218, 2016 | 599 | 2016 |
| Tools and benchmarks for automated log parsing. J Zhu, S He, J Liu, P He, Q Xie, Z Zheng, MR Lyu ICSE, 2019 | 473* | 2019 |
| An evaluation study on log parsing and its use in log mining P He, J Zhu, S He, J Li, MR Lyu DSN 2016, 654-661, 2016 | 291 | 2016 |
| Loghub: A large collection of system log datasets towards automated log analytics S He, J Zhu, P He, MR Lyu arXiv preprint arXiv:2008.06448, 2020 | 220 | 2020 |
| Towards automated log parsing for large-scale log data analysis P He, J Zhu, S He, J Li, MR Lyu IEEE TDSC 15 (6), 931-944, 2017 | 220 | 2017 |
| Identifying impactful service system problems via log analysis S He, Q Lin, JG Lou, H Zhang, MR Lyu, D Zhang ESEC/FSE 2018, 60-70, 2018 | 173 | 2018 |
| A Survey on Automated Log Analysis for Reliability Engineering S He, P He, Z Chen, T Yang, Y Su, MR Lyu ACM Computing Surveys (CSUR), 2021 | 167 | 2021 |
| Characterizing the natural language descriptions in software logging statements P He, Z Chen, S He, MR Lyu ASE 2018, 178-189, 2018 | 93 | 2018 |
| Logzip: Extracting Hidden Structures via Iterative Clustering for Log Compression J Liu, J Zhu, S He, P He, Z Zheng, MR Lyu ASE 2019, 2019 | 65 | 2019 |
| UniParser: A Unified Log Parser for Heterogeneous Log Data Y Liu, X Zhang, S He, H Zhang, L Li, Y Kang, Y Xu, M Ma, Q Lin, Y Dang, ... WWW 2022, 2022 | 49 | 2022 |
| Towards Understanding Neural Machine Translation with Word Importance S He, Z Tu, X Wang, L Wang, MR Lyu, S Shi EMNLP 2019, 2019 | 41 | 2019 |
| Multi-Task Learning with Shared Encoder for Non-Autoregressive Machine Translation Y Hao, S He, W Jiao, Z Tu, M Lyu, X Wang NAACL 2021, 2020 | 27 | 2020 |
| Fighting the Fog of War: Automated Incident Detection for Cloud Systems L Li, X Zhang, X Zhao, P Zhao, B Qiao, S He, P Lee, J Sun, F Gao, L Yang, ... USENIX ATC 2021, 131-146, 2021 | 25 | 2021 |
| SPINE: A Scalable Log Parser with Feedback Guidance X Wang, X Zhang, L Li, S He, H Zhang, Y Liu, L Zheng, Y Kang, Q Lin, ... ESEC/FSE 2022, 1198-1208, 2022 | 24 | 2022 |
| Onion: identifying incident-indicating logs for cloud systems X Zhang, Y Xu, S Qin, S He, B Qiao, Z Li, H Zhang, X Li, Y Dang, Q Lin, ... ESEC/FSE 2021, 1253-1263, 2021 | 24 | 2021 |
| Data Rejuvenation: Exploiting Inactive Training Examples for Neural Machine Translation W Jiao, X Wang, S He, I King, MR Lyu, Z Tu EMNLP 2020, 2020 | 22 | 2020 |
| An Empirical Investigation of Missing Data Handling in Cloud Node Failure Prediction M Ma, Y Liu, Y Tong, H Li, P Zhao, Y Xu, H Zhang, S He, L Wang, Y Dang, ... ESEC/FSE 2022, 1453-1464, 2022 | 14 | 2022 |
| Imdiffusion: Imputed diffusion models for multivariate time series anomaly detection Y Chen, C Zhang, M Ma, Y Liu, R Ding, B Li, S He, S Rajmohan, Q Lin, ... arXiv preprint arXiv:2307.00754, 2023 | 13 | 2023 |
| Did We Miss Something Important? Studying and Exploring Variable-Aware Log Abstraction Z Li, C Luo, THP Chen, W Shang, S He, Q Lin, D Zhang ICSE, 2023 | 13 | 2023 |
| An intelligent framework for timely, accurate, and comprehensive cloud incident detection Y Li, X Zhang, S He, Z Chen, Y Kang, J Liu, L Li, Y Dang, F Gao, Z Xu, ... ACM SIGOPS Operating Systems Review 56 (1), 1-7, 2022 | 12 | 2022 |
Michael R. LyuProfessor of Computer Science & Engineering, The Chinese University of Hong KongVerified email at cse.cuhk.edu.hk
