25 citations · 54 across the 19 of their papers we have counts for
20 papers
Cascaded Code Editing: Large-Small Model Collaboration for Effective and Efficient Code Editing
Chaozheng Wang, Zezhou Yang, Shuzheng Gao +7
Code editing constitutes a fundamental practice in software development, wherein developers modify existing codebases according to natural language requirements. Accurate code edit…
TypeScript Repository Indexing for Code Agent Retrieval
Junsong Pu, Yichen Li, Zhuangbin Chen
Graph-based code indexing can improve context retrieval for LLM-based code agents by preserving call chains and dependency relationships that keyword search and similarity retrieva…
Understanding Secret Leakage Risks in Code LLMs: A Tokenization Perspective
Meifang Chen, Zhe Yang, Huang Nianchen +4
Code secrets are sensitive assets for software developers, and their leakage poses significant cybersecurity risks. While the rapid development of AI code assistants powered by Cod…
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…
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…
Exploring Autonomous Agents: A Closer Look at Why They Fail When Completing Tasks
Ruofan Lu, Yichen Li, Yintong Huo
Autonomous agent systems powered by Large Language Models (LLMs) have demonstrated promising capabilities in automating complex tasks. However, current evaluations largely rely on…
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…
LogUpdater: Automated Detection and Repair of Specific Defects in Logging Statements
Renyi Zhong, Yichen Li, Jinxi Kuang +3
Developers use logging statements to track software runtime behaviors and system status. Yet, unclear or misleading logs can hide true execution patterns and hinder software mainte…
Face It Yourselves: An LLM-Based Two-Stage Strategy to Localize Configuration Errors via Logs
Shiwen Shan, Yintong Huo, Yuxin Su +3
Configurable software systems are prone to configuration errors, resulting in significant losses to companies. However, diagnosing these errors is challenging due to the vast and c…
FaultProfIT: Hierarchical Fault Profiling of Incident Tickets in Large-scale Cloud Systems
Junjie Huang, Jinyang Liu, Zhuangbin Chen +7
Postmortem analysis is essential in the management of incidents within cloud systems, which provides valuable insights to improve system's reliability and robustness. At CloudA, fa…
Go Static: Contextualized Logging Statement Generation
Yichen Li, Yintong Huo, Renyi Zhong +6
Logging practices have been extensively investigated to assist developers in writing appropriate logging statements for documenting software behaviors. Although numerous automatic…
Enhancing LLM-Based Coding Tools through Native Integration of IDE-Derived Static Context
Yichen Li, Yun Peng, Yintong Huo +1
Large Language Models (LLMs) have achieved remarkable success in code completion, as evidenced by their essential roles in developing code assistant services such as Copilot. Being…
MTAD: Tools and Benchmarks for Multivariate Time Series Anomaly Detection
Jinyang Liu, Wenwei Gu, Zhuangbin Chen +3
Key Performance Indicators (KPIs) are essential time-series metrics for ensuring the reliability and stability of many software systems. They faithfully record runtime states to fa…
LILAC: Log Parsing using LLMs with Adaptive Parsing Cache
Zhihan Jiang, Jinyang Liu, Zhuangbin Chen +6
Log parsing transforms log messages into structured formats, serving as the prerequisite step for various log analysis tasks. Although a variety of log parsing approaches have been…