25 citations · 54 across the 19 of their papers we have counts for
8 papers · 2 filters
AutoLogger: A Multi-Agent Framework for the End-to-End Automated Logging
Renyi Zhong, Yintong Huo, Wenwei Gu +2
Software logging is critical for system observability, yet developers face a dual crisis of costly overlogging and risky underlogging. Existing automated logging tools often overlo…
Trace Sampling 2.0: Code Knowledge Enhanced Span-level Sampling for Distributed Tracing
Yulun Wu, Guangba Yu, Zhihan Jiang +2
Distributed tracing is an essential diagnostic tool in microservice systems, but the sheer volume of traces places a significant burden on backend storage. A common approach to mit…
Next Edit Prediction: Learning to Predict Code Edits from Context and Interaction History
Ruofan Lu, Yintong Huo, Meng Zhang +2
The rapid advancement of large language models (LLMs) has led to the widespread adoption of AI-powered coding assistants integrated into a development environment. On one hand, low…
CCISolver: End-to-End Detection and Repair of Method-Level Code-Comment Inconsistency
Renyi Zhong, Yintong Huo, Wenwei Gu +6
Comments within code serve as a crucial foundation for software documentation, facilitating developers to communicate and understand the code effectively. However, code-comment inc…
Larger Is Not Always Better: Exploring Small Open-source Language Models in Logging Statement Generation
Renyi Zhong, Yichen Li, Guangba Yu +4
Developers use logging statements to create logs that document system behavior and aid in software maintenance. As such, high-quality logging is essential for effective maintenance…
LLMPrism: Black-box Performance Diagnosis for Production LLM Training Platforms
Zhihan Jiang, Rui Ren, Guangba Yu +8
Large Language Models (LLMs) have brought about revolutionary changes in diverse fields, rendering LLM training of utmost importance for modern enterprises. To meet this demand, mu…
COCA: Generative Root Cause Analysis for Distributed Systems with Code Knowledge
Yichen Li, Yulun Wu, Jinyang Liu +4
Runtime failures are commonplace in modern distributed systems. When such issues arise, users often turn to platforms such as Github or JIRA to report them and request assistance.…
L4: Diagnosing Large-scale LLM Training Failures via Automated Log Analysis
Zhihan Jiang, Junjie Huang, Zhuangbin Chen +6
As Large Language Models (LLMs) show their capabilities across various applications, training customized LLMs has become essential for modern enterprises. However, due to the compl…