All papers that have not been peer-reviewed will not appear here, including preprints. You can access my all of papers at đź”—Google Scholar.

2026

CRFD: Causal and Interpretable Fault Diagnosis Using Counterfactual Reasoning for Microservices on Multi-source Observability Data

Xiangbo Tian, Shi Ying, Tiangang Li, Chuan Shi, Ding Xiao

ACM Transactions on Software Engineering and Methodology (TOSEM) 2026 JournalCCF-A

We propose CRFD, a causal and interpretable fault diagnosis approach based on multi-source observability data for microservice systems, which uses counterfactual reasoning to diagnose faults and provide effective guidance for subsequent troubleshooting.

[Paper] [Code]

CRFD: Causal and Interpretable Fault Diagnosis Using Counterfactual Reasoning for Microservices on Multi-source Observability Data

Xiangbo Tian, Shi Ying, Tiangang Li, Chuan Shi, Ding Xiao

ACM Transactions on Software Engineering and Methodology (TOSEM) 2026 JournalCCF-A

We propose CRFD, a causal and interpretable fault diagnosis approach based on multi-source observability data for microservice systems, which uses counterfactual reasoning to diagnose faults and provide effective guidance for subsequent troubleshooting.

[Paper] [Code]

CausalLog: Log parsing using LLMs with causal intervention for bias mitigation

Yuan Tian, Shi Ying, Tiangang Li

Information Processing & Management (IPM) 2024 JournalCCF-A

We propose CausalLog, a lightweight and flexible debiasing framework for log parsing.

[Paper]

CausalLog: Log parsing using LLMs with causal intervention for bias mitigation

Yuan Tian, Shi Ying, Tiangang Li

Information Processing & Management (IPM) 2024 JournalCCF-A

We propose CausalLog, a lightweight and flexible debiasing framework for log parsing.

[Paper]

2025

DALAD: Unsupervised Detection of Global and Local Anomalies in Microservice Systems

Xiangbo Tian, Shi Ying, Tiangang Li, Ting Zhang, Yong Wang

IEEE Transactions on Services Computing (TSC) 2025 JournalCCF-A

We propose DALAD, a novel distribution-adversarial-learning-based anomaly detection approach for microservice systems.

[Paper] [Code]

DALAD: Unsupervised Detection of Global and Local Anomalies in Microservice Systems

Xiangbo Tian, Shi Ying, Tiangang Li, Ting Zhang, Yong Wang

IEEE Transactions on Services Computing (TSC) 2025 JournalCCF-A

We propose DALAD, a novel distribution-adversarial-learning-based anomaly detection approach for microservice systems.

[Paper] [Code]

ASTRA: Adversarial Sim-to-Real Transfer Reinforcement Learning for Autoscaling in Cloud Systems

Tiangang Li, Shi Ying, Xiangbo Tian, Ting Zhang, Yong Wang

IEEE Transactions on Software Engineering (TSE) 2025 JournalCCF-A

We propose ASTRA, a sim-to-real transfer reinforcement learning framework for autoscaling.

[Paper] [Code]

ASTRA: Adversarial Sim-to-Real Transfer Reinforcement Learning for Autoscaling in Cloud Systems

Tiangang Li, Shi Ying, Xiangbo Tian, Ting Zhang, Yong Wang

IEEE Transactions on Software Engineering (TSE) 2025 JournalCCF-A

We propose ASTRA, a sim-to-real transfer reinforcement learning framework for autoscaling.

[Paper] [Code]

SAC-based Ensemble Framework for Multi-view Workload Forecasting in Cloud Computing

Wenxuan Zeng, Shi Ying, Tiangang Li, Xiangbo Tian, Yuhong Jiang, Hujie Liu, Shikui Hao

Journal of Software (in Chinese) 2025 JournalChinese CCF-A

We propose SAC-MWF, a multi-view workload forecast ensemble framework based on Soft Actor-Critic (SAC) algorithm.

[Paper]

SAC-based Ensemble Framework for Multi-view Workload Forecasting in Cloud Computing

Wenxuan Zeng, Shi Ying, Tiangang Li, Xiangbo Tian, Yuhong Jiang, Hujie Liu, Shikui Hao

Journal of Software (in Chinese) 2025 JournalChinese CCF-A

We propose SAC-MWF, a multi-view workload forecast ensemble framework based on Soft Actor-Critic (SAC) algorithm.

[Paper]

DebiasParser: Debiasing LLM-Based Log Parsing via Front-Door Adjustment

Yuan Tian, Shi Ying, Tiangang Li

International Conference on Intelligent Computing (ICIC) 2025 ConferenceCCF-C

We propose DebiasParser, a novel log parsing framework that incorporates a debiasing mechanism grounded in Structural Causal Models (SCMs) and implemented via front-door adjustment.

[Paper]

DebiasParser: Debiasing LLM-Based Log Parsing via Front-Door Adjustment

Yuan Tian, Shi Ying, Tiangang Li

International Conference on Intelligent Computing (ICIC) 2025 ConferenceCCF-C

We propose DebiasParser, a novel log parsing framework that incorporates a debiasing mechanism grounded in Structural Causal Models (SCMs) and implemented via front-door adjustment.

[Paper]

2024

iTCRL: Causal-Intervention-Based Trace Contrastive Representation Learning for Microservice Systems

Xiangbo Tian, Shi Ying, Tiangang Li, Mengting Yuan, Ruijin Wang, Yishi Zhao

IEEE Transactions on Software Engineering (TSE) 2024 JournalCCF-A

We propose iTCRL, a novel trace contrastive representation learning approach based on causal intervention.

[Paper]

iTCRL: Causal-Intervention-Based Trace Contrastive Representation Learning for Microservice Systems

Xiangbo Tian, Shi Ying, Tiangang Li, Mengting Yuan, Ruijin Wang, Yishi Zhao

IEEE Transactions on Software Engineering (TSE) 2024 JournalCCF-A

We propose iTCRL, a novel trace contrastive representation learning approach based on causal intervention.

[Paper]

Batch Jobs Load Balancing Scheduling in Cloud Computing Using Distributional Reinforcement Learning

Tiangang Li, Shi Ying, Yishi Zhao, Jianga Shang

IEEE Transactions on Parallel and Distributed (TPDS) 2024 JournalCCF-A

We propose a Distributional Reinforcement Learning–based dynamic load balancing algorithm for cloud batch job scheduling, which outperforms existing baselines in load balance, task success rate, and completion time on real Alibaba cluster traces.

[Paper]

Batch Jobs Load Balancing Scheduling in Cloud Computing Using Distributional Reinforcement Learning

Tiangang Li, Shi Ying, Yishi Zhao, Jianga Shang

IEEE Transactions on Parallel and Distributed (TPDS) 2024 JournalCCF-A

We propose a Distributional Reinforcement Learning–based dynamic load balancing algorithm for cloud batch job scheduling, which outperforms existing baselines in load balance, task success rate, and completion time on real Alibaba cluster traces.

[Paper]

2021

Adversarial examples attack based on random warm restart mechanism and improved Nesterov momentum

Tiangang Li

arXiv 2021 Preprint

We propose RWR-NM-PGD attack algorithm based on random warm restart mechanism and improved Nesterov momentum from the view of gradient optimization.

[Paper] [Code]

Adversarial examples attack based on random warm restart mechanism and improved Nesterov momentum

Tiangang Li

arXiv 2021 Preprint

We propose RWR-NM-PGD attack algorithm based on random warm restart mechanism and improved Nesterov momentum from the view of gradient optimization.

[Paper] [Code]