Distributed tracing is essential for observing microservices but incurs prohibitive storage costs. Existing solutions primarily rely on trace-level sampling, which indiscriminately discards entire traces based on probability or tail latency. This coarse-grained approach often loses critical off-the-path anomalies and fails to retain normal baselines required for comparative diagnosis. To address these limitations, we introduce the concept of span-level sampling, a fine-grained paradigm that retains only essential spans rather than full traces. However, we identify a critical challenge in this new approach where naive span retention leads to structural ambiguity, as the removal of intermediate spans disrupts the graph topology and renders the remaining data useless for analysis. To resolve this, we propose AutoScope , a novel framework that leverages static code knowledge to enable structurally safe span-level sampling. AutoScope constructs a Bridged Call Site Control Flow Graph (CSCFG) to map runtime spans to static execution logic. By identifying Dominant Span Sets (DSS), it exploits the “Sample One, Infer All” property to enable aggressive compression while preserving the structural skeleton of every request. Extensive evaluations on two benchmark microservice systems demonstrate that AutoScope achieves an 80.2% storage reduction while maintaining a state-of-the-art faulty span coverage of 98.1%. Furthermore, the reconstructed traces enhance downstream Root Cause Analysis (RCA) tasks, improving the Mean Reciprocal Rank (MRR) by an average of 8.3%.
Yulun Wu, Guangba Yu, Zhihan Jiang et al.· ACM Transactions on Software...· 0 citations
System logs are critical for software reliability. While many automated log-based anomaly detection methods exist, they often falter in large-scale cloud systems due to high resource consumption and poor adaptability to evolving logs. In this paper, we present SeaLog, an accurate, lightweight, and adaptive log-based anomaly detection framework that synergistically integrates the efficiency of traditional data-driven methods with the zero/few-shot learning ability of large language models (LLMs). SeaLog consists of a lightweight detection agent and a backbone analyzer. The detection agent utilizes an n-gram probabilistic tree (NPT) for efficient anomaly detection. The backbone analyzer employs an LLM enhanced by in-context learning (ICL) to provide accurate and adaptive predictions, especially for unseen log data. Both components continuously benefit from anomalies confirmed by on-site engineers. We evaluate SeaLog on three public datasets (BGL, Thunderbird, and HDFS) and an industrial dataset, with multiple LLM backbones, including open-source models, to assess its robustness to model choice. SeaLog achieves strong effectiveness across all datasets (F1 scores of 0.949–0.999), runs 2× to 5× faster, and consumes only 5% to 41% of the memory resources. Furthermore, we have successfully deployed SeaLog in Huawei Cloud, from which we share our insights and experiences during this deployment.
Jinyang Liu, Junjie Huang, Zhihan Jiang et al.· ACM Transactions on Software...· 0 citations
SmartRAG is presented, a fully on-device framework that organizes an intelligent assistant around four coordinated modules -- Perception, Memory, Focus, and Thinking -- keeping inference costs bounded and at the core of EvoNER, a continually learnable named-entity recognizer that incrementally expands its label inventory through teacher-distilled updates.
Zhihan Jiang, Meng Li, Shenghao Liu et al.· arXiv.org· 0 citations
This work presents AutoSQL, a system that reconstructs SQL templates from Go ORM code that constructs a Code Index, a directed graph that captures structural dependencies between functions, types, and global variables as navigable edges and synthesizes SQL templates.
Jun-Song Pu, Yichen Li, Zhuang-Bin Chen et al.· 0 citations
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