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Nezha: Interpretable Fine-Grained Root Causes Analysis for Microservices on Multi-modal Observability Data.

, , , , , and . ESEC/SIGSOFT FSE, page 553-565. ACM, (2023)

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LogReducer: Identify and Reduce Log Hotspots in Kernel on the Fly., , , , , , and . ICSE, page 1763-1775. IEEE, (2023)CCISolver: End-to-End Detection and Repair of Method-Level Code-Comment Inconsistency., , , , , , , , and . CoRR, (June 2025)Larger Is Not Always Better: Exploring Small Open-source Language Models in Logging Statement Generation., , , , , , and . CoRR, (May 2025)A Framework of Virtual War Room and Matrix Sketch-Based Streaming Anomaly Detection for Microservice Systems., , and . IEEE Access, (2020)T-Rank: A Lightweight Spectrum based Fault Localization Approach for Microservice Systems., , and . CCGRID, page 416-425. IEEE, (2021)FaaSRCA: Full Lifecycle Root Cause Analysis for Serverless Applications., , , , , and . ISSRE, page 415-426. IEEE, (2024)Nezha: Interpretable Fine-Grained Root Causes Analysis for Microservices on Multi-modal Observability Data., , , , , and . ESEC/SIGSOFT FSE, page 553-565. ACM, (2023)TraStrainer: Adaptive Sampling for Distributed Traces with System Runtime State., , , , , , , and . Proc. ACM Softw. Eng., 1 (FSE): 473-493 (2024)L4: Diagnosing Large-scale LLM Training Failures via Automated Log Analysis., , , , , , , , , and . SIGSOFT FSE Companion, page 51-63. ACM, (2025)Mint: Cost-Efficient Tracing with All Requests Collection via Commonality and Variability Analysis., , , , , , , , and . ASPLOS (1), page 683-697. ACM, (2025)