Large Language Model (LLM) services expose a large configuration space that critically affects performance, yet tuning these configurations on hardware (e.g., NVIDIA GPUs) is prohibitively expensive. Simulation offers a scalable alternative, but state-of-the-art simulators suffer from a fundamental software engineering...
Yu-Jie Huang, Jia-Zhen Gu, Zhihan Jiang et al.· Proceedings of the 41st IEEE...· 0 citations
This work proposes AutoScope, a novel framework that leverages static code knowledge to enable structurally safe span-level sampling and introduces the concept of span-level sampling, a fine-grained paradigm that retains only essential spans rather than full traces.
Yu-Lun Wu, Guangba Yu, Zhihan Jiang et al.· ACM Transactions on Software...· 0 citations
SeaLog is presented, 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).
Jinyang Liu, Jun-Jie 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 invento...
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, Yi-Chen Li, Zhuang-Bin Chen et al.· Proceedings of the 41st IEEE...· 0 citations
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