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cs.SE2026
Automated Logging Is Language-Sensitive: A Multilingual Benchmark and Empirical Study of LLMs
Renyi Zhong, Yichen Li, Yulun Wu +3
Logging statements are central to debugging, failure diagnosis, and production observability, yet writing them requires developers to decide where to place a logging statement, whi…
cs.SE2026
Why Does the LLM Stop Computing: An Empirical Study of User-Reported Failures in Open-Source LLMs
Guangba Yu, Zirui Wang, Yujie Huang +4
The democratization of open-source Large Language Models (LLMs) allows users to fine-tune and deploy models on local infrastructure but exposes them to a First Mile deployment land…