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Distributed databases are critical infrastructures for today's large-scale software systems, making effective failure management essential to ensure software availability.
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Identifying bad software changes via multimodal anomaly detection for online service systems. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 527–539
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Recommending root-cause and mitigation steps for cloud incidents using large language models. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 1737–1749
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Heterogeneous anomaly detection for software systems via semi-supervised cross-modal attention. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 1724–1736
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Towards Close-To-Zero Runtime Collection Overhead: Raft-Based Anomaly Diagnosis on System Faults for Distributed Storage System
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A Survey of AIOps for Failure Management in the Era of Large Language Models
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Time-tired compaction: An elastic compaction scheme for LSM-tree based time-series database
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Robust failure diagnosis of microservice system through multimodal data
Shenglin Zhang, Pengxiang Jin, Zihan Lin, Yongqian Sun, Bicheng Zhang, Sibo Xia, Zhengdan Li, Zhenyu Zhong, Minghua Ma, Wa Jin, et al · 2023
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X-lifecycle learning for cloud incident management using llms. In Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering . 417–428
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Autonomous monitors for detecting failures early and reporting interpretable alerts in cloud operations. In Proceedings of the 46th International Conference on Software Engineering: Software Engineering in Practice . 47–57
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Root Cause Analysis in Microservice Using Neural Granger Causal Discovery. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 38. 206–213
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Exploring llm-based agents for root cause analysis. In Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering . 208–219
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LM-PACE: Confidence estimation by large language models for effective root causing of cloud incidents. In Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering . 388–398
Dylan Zhang, Xuchao Zhang, Chetan Bansal, Pedro Las-Casas, Rodrigo Fonseca, and Saravan Rajmohan. 2024h
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Multivariate Log-based Anomaly Detection for Distributed Database. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 4256–4267
Lingzhe Zhang, Tong Jia, Mengxi Jia, Ying Li, Yong Yang, and Zhonghai Wu. 2024d
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Reducing Events to Augment Log-based Anomaly Detection Models: An Empirical Study. In Proceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement . 538–548
Lingzhe Zhang, Tong Jia, Kangjin Wang, Mengxi Jia, Yong Yang, and Ying Li. 2024g
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Wei Zhang, Hongcheng Guo, Jian Yang, Yi Zhang, Chaoran Yan, Zhoujin Tian, Hangyuan Ji, Zhoujun Li, Tongliang Li, Tieqiao Zheng, et al · 2024
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Multi-modal Causal Structure Learning and Root Cause Analysis
Lecheng Zheng, Zhengzhang Chen, Jingrui He, and Haifeng Chen. 2024 · 2024
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ScalaLog: Scalable Log-Based Failure Diagnosis Using LLM. In ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 1–5
Lingzhe Zhang, Tong Jia, Mengxi Jia, Yifan Wu, Hongyi Liu, and Ying Li. 2025a · 2025
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XRAGLog: A Resource-Efficient and Context-Aware Log-Based Anomaly Detection Method Using Retrieval-Augmented Generation. In AAAI 2025 Workshop on Preventing and Detecting LLM Misinformation (PDLM)
Lingzhe Zhang, Tong Jia, Mengxi Jia, Yifan Wu, Hongyi Liu, and Ying Li. 2025b · 2025
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