works on

From the 1 of 19 linked papers with an AI index.

activity
20242026
most citedSecureVibeBench: Benchmarking Secure Vibe Coding of AI Agents via Reconstructing Vulnerability-Introducing Scenarios

2 citations · 2 across the 3 of their papers we have counts for

collaborators

19 papers

cs.SE2026

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…

cs.SE2026

SWE-NFI: Studying and Benchmarking Coding Agents for Non-Functional Improvements

Pengyu Xue, He Yang Yuan, Xin Wang +6

The paper introduces SWE-NFI, a benchmark that assesses how coding agents can make non-functional, behavior-preserving improvements to Python code, using real pull‑request tasks an…

cs.SE20262 cited

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…

cs.SE2026

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…

cs.AI2026

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…

cs.SE2026

ReLog: Execution-Aware Logging with Runtime Feedback for LLM-Oriented Debugging

Xin Wang, Yang Feng, Xiaoqian Jiao +4

Logging statements are essential for software debugging and maintenance. However, existing approaches to automatic logging generation rely on static analysis and produce statements…