20 citations · 47 across the 16 of their papers we have counts for
12 papers · 1 filter
Bifrost: Empowering Pretrained Language Model with Fallibility Representation for Log-Based Fault Diagnosis
Minghua He, Tong Jia, Lingzhe Zhang +9
Log-based fault diagnosis is crucial for runtime debugging and maintenance. Existing fault diagnosis methods use language models pre-trained on natural language (PLMs) for log repr…
Towards In-Depth Root Cause Localization for Microservices with Multi-Agent Recursion-of-Thought
Lingzhe Zhang, Tong Jia, Kangjin Wang +8
As modern microservice systems grow increasingly complex due to dynamic interactions and evolving runtime environments, they experience failures with rising frequency. Ensuring sys…
Towards Robust LLM Post-Training: Automatic Failure Management for Reinforcement Fine-Tuning
Lingzhe Zhang, Tong Jia, Yunpeng Zhai +6
Reinforcement fine-tuning (RFT) has become a core paradigm for post-training large language models, yet its training process remains highly fragile. Existing efforts mainly improve…
United We Stand: Towards End-to-End Log-based Fault Diagnosis via Interactive Multi-Task Learning
Minghua He, Chiming Duan, Pei Xiao +8
Log-based fault diagnosis is essential for maintaining software system availability. However, existing fault diagnosis methods are built using a task-independent manner, which fail…
Walk the Talk: Is Your Log-based Software Reliability Maintenance System Really Reliable?
Minghua He, Tong Jia, Chiming Duan +6
Log-based software reliability maintenance systems are crucial for sustaining stable customer experience. However, existing deep learning-based methods represent a black box for se…
Adaptive Root Cause Localization for Microservice Systems with Multi-Agent Recursion-of-Thought
Lingzhe Zhang, Tong Jia, Kangjin Wang +4
As contemporary microservice systems become increasingly popular and complex-often comprising hundreds or even thousands of fine-grained, interdependent subsystems-they are facing…
A Survey of AIOps in the Era of Large Language Models
Lingzhe Zhang, Tong Jia, Mengxi Jia +7
As large language models (LLMs) grow increasingly sophisticated and pervasive, their application to various Artificial Intelligence for IT Operations (AIOps) tasks has garnered sig…
AgentFM: Role-Aware Failure Management for Distributed Databases with LLM-Driven Multi-Agents
Lingzhe Zhang, Yunpeng Zhai, Tong Jia +3
Distributed databases are critical infrastructures for today's large-scale software systems, making effective failure management essential to ensure software availability. However,…
ThinkFL: Self-Refining Failure Localization for Microservice Systems via Reinforcement Fine-Tuning
Lingzhe Zhang, Yunpeng Zhai, Tong Jia +6
As modern microservice systems grow increasingly popular and complex-often consisting of hundreds or even thousands of fine-grained, interdependent components-they are becoming mor…
Reducing Events to Augment Log-based Anomaly Detection Models: An Empirical Study
Lingzhe Zhang, Tong Jia, Kangjin Wang +3
As software systems grow increasingly intricate, the precise detection of anomalies have become both essential and challenging. Current log-based anomaly detection methods depend h…
A Survey of AIOps for Failure Management in the Era of Large Language Models
Lingzhe Zhang, Tong Jia, Mengxi Jia +7
As software systems grow increasingly intricate, Artificial Intelligence for IT Operations (AIOps) methods have been widely used in software system failure management to ensure the…
Multivariate Log-based Anomaly Detection for Distributed Database
Lingzhe Zhang, Tong Jia, Mengxi Jia +3
Distributed databases are fundamental infrastructures of today's large-scale software systems such as cloud systems. Detecting anomalies in distributed databases is essential for m…