27 citations · 42 across the 24 of their papers we have counts for
28 papers
Towards Automatically Pruning Logging Code with Coding Agents: How Far Are We?
He Yang Yuan, Haonan Zhang, Xin Wang +4
Logging code supports debugging, monitoring, and software maintenance, but excessive logging can add noise, impose runtime overhead, and obscure diagnostic information. While prior…
Do Tool Calls Execute as Intended? Measuring and Repairing Intent-Execution Correspondence in LLM Agents
Boyang Yang, Zhenhao Li, Ziyao Yang +4
Agents built on large language models (LLMs) build and run software through tool calls. A call reaches its program through several hops, and any hop can change the call without not…
TAILOR: Template-Preserving Augmentation for Long-Tailed Log Parsing
Sepideh Hodaeian, Zhenhao Li, An Ran Chen
Log parsing is essential for system log analysis because it supports tasks such as debugging, monitoring, and anomaly detection by transforming unstructured log messages into struc…
AgentExecutor: Partial Code Execution via Agentic Context Generation
Junkai Chen, Chengran Yang, Xing Hu +3
Executing code snippets is essential for dynamic program analysis, but it remains challenging to execute an arbitrary code snippet due to issues like missing context and incomplete…
SWE-NFI: Studying and Benchmarking Coding Agents for Non-Functional Improvements
Pengyu Xue, He Yang Yuan, Xin Wang +6
Although coding agents have achieved impressive performance on correctness-oriented benchmarks, their ability to make behavior-preserving non-functional improvements (NFIs) remains…
Towards Secure Logging: Characterizing and Benchmarking Logging Code Security Issues with LLMs
He Yang Yuan, Xin Wang, Kundi Yao +3
Logging code plays an important role in software systems by recording key events and behaviors, which are essential for debugging and monitoring. However, insecure logging practice…
How Adversarial Environments Mislead Agentic AI?
Zhonghao Zhan, Huichi Zhou, Zhenhao Li +3
Tool-integrated agents are deployed on the premise that external tools ground their outputs in reality. Yet this very reliance creates a critical attack surface. Current evaluation…
ReLog: Execution-Aware Logging with Runtime Feedback for LLM-Oriented Debugging
Xin Wang, Yang Feng, Xiaoqian Jiao +3
Logging statements are essential for software debugging and maintenance. However, existing approaches to automatic logging generation rely on static analysis and produce statements…
LLM4Perf: Large Language Models Are Effective Samplers for Multi-Objective Performance Modeling
Xin Wang, Zhenhao Li, Zishuo Ding
The performance of modern software systems is critically dependent on their complex configuration options. Building accurate performance models to navigate this vast space requires…
Evaluating Software Process Models for Multi-Agent Class-Level Code Generation
Wasique Islam Shafin, Md Nakhla Rafi, Zhenhao Li +1
Modern software systems require code that is not only functional but also maintainable and well-structured. Although Large Language Models (LLMs) are increasingly used to automate…
Enhancing LLM-based Fault Localization with a Functionality-Aware Retrieval-Augmented Generation Framework
Xinyu Shi, Zhenhao Li, An Ran Chen
Fault localization (FL) is a critical but time-consuming task in software debugging, aiming to identify faulty code elements. While recent advances in large language models (LLMs)…
SecureVibeBench: Benchmarking Secure Vibe Coding of AI Agents via Reconstructing Vulnerability-Introducing Scenarios
Junkai Chen, Huihui Huang, Yunbo Lyu +10
Large language model-powered code agents are rapidly transforming software engineering, yet the security risks of their generated code have become a critical concern. Existing benc…
Defects4Log: Benchmarking LLMs for Logging Code Defect Detection and Reasoning
Xin Wang, Zhenhao Li, Zishuo Ding
Logging code is written by developers to capture system runtime behavior and plays a vital role in debugging, performance analysis, and system monitoring. However, defects in loggi…
RobuNFR: Evaluating the Robustness of Large Language Models on Non-Functional Requirements Aware Code Generation
Feng Lin, Dong Jae Kim, Zhenhao Li +3
When using LLMs to address Non-Functional Requirements (NFRs), developers may behave differently (e.g., expressing the same NFR in different words). Robust LLMs should output consi…
An Empirical Study of Retrieval-Augmented Code Generation: Challenges and Opportunities
Zezhou Yang, Sirong Chen, Cuiyun Gao +4
Code generation aims to automatically generate code snippets of specific programming language according to natural language descriptions. The continuous advancements in deep learni…
TrustRAG: Enhancing Robustness and Trustworthiness in Retrieval-Augmented Generation
Huichi Zhou, Kin-Hei Lee, Zhonghao Zhan +5
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge sources, enabling more accurate and contextually relevant responses tai…
Discovery of Timeline and Crowd Reaction of Software Vulnerability Disclosures
Yi Wen Heng, Zeyang Ma, Haoxiang Zhang +3
Reusing third-party libraries increases productivity and saves time and costs for developers. However, the downside is the presence of vulnerabilities in those libraries, which can…
Studying and Benchmarking Large Language Models For Log Level Suggestion
Yi Wen Heng, Zeyang Ma, Zhenhao Li +3
Large Language Models (LLMs) have become a focal point of research across various domains, including software engineering, where their capabilities are increasingly leveraged. Rece…
Understanding Practitioners' Expectations on Clear Code Review Comments
Junkai Chen, Zhenhao Li, Qiheng Mao +3
The code review comment (CRC) is pivotal in the process of modern code review. It provides reviewers with the opportunity to identify potential bugs, offer constructive feedback, a…
ThinkRepair: Self-Directed Automated Program Repair
Xin Yin, Chao Ni, Shaohua Wang +3
Though many approaches have been proposed for Automated Program Repair (APR) and indeed achieved remarkable performance, they still have limitations in fixing bugs that require ana…
NLPerturbator: Studying the Robustness of Code LLMs to Natural Language Variations
Junkai Chen, Zhenhao Li, Xing Hu +1
Large language models (LLMs) achieve promising results in code generation based on a given natural language description. They have been integrated into open-source projects and com…
Towards Explainable Vulnerability Detection with Large Language Models
Qiheng Mao, Zhenhao Li, Xing Hu +3
Software vulnerabilities pose significant risks to the security and integrity of software systems. Although prior studies have explored vulnerability detection using deep learning…
A Roadmap on Modern Code Review: Challenges and Opportunities
Zezhou Yang, Cuiyun Gao, Zhaoqiang Guo +4
Over the past decade, modern code review (MCR) has been established as a cornerstone of software quality assurance and a vital channel for knowledge transfer within development tea…
Reasoning Runtime Behavior of a Program with LLM: How Far Are We?
Junkai Chen, Zhiyuan Pan, Xing Hu +3
Large language models for code (i.e., code LLMs) have shown strong code understanding and generation capabilities. To evaluate the capabilities of code LLMs in various aspects, man…