22 citations · 27 across the 15 of their papers we have counts for
16 papers
When Code Becomes an LLM Call: An Empirical Study of Call-Site Contracts in Agentic Systems
Shihao Weng, Longtian Wang, Yang Feng +6
Software quality has long rested on specifications that state what code must do, and on testing and verification that check that the code does it. Agentic systems change this found…
Learning from the Test: Self-Referential Differential Testing for Deep RL Agents
Junda He, Jieke Shi, Zhou Yang +2
Deep Reinforcement Learning (DRL) has achieved significant success in complex decision-making problems. As DRL systems are increasingly deployed in real-world applications, ensurin…
PRWeaver: Evaluating LLM-Based Code Auditors against Long-Horizon Malicious Pull Requests
Yuekun Wang, Mingfei Cheng, Xiaofei Xie
LLM-based code auditors are increasingly integrated into pull-request (PR) workflows, yet their reliability against adversarial changes distributed across repository evolution rema…
EvoEye: Self-Evolving Runtime Monitoring for Autonomous Driving Systems
Mingfei Cheng, Lionel Briand, Xiaofei Xie
Runtime monitoring is essential for detecting impending hazards in autonomous driving systems (ADSs). However, existing ADS runtime monitors have fixed detection capabilities: rule…
Ensemble-Based Uncertainty Estimation for Code Correctness Estimation
Yunxiang Wei, Tianlin Li, Yuwei Zheng +6
Large language models (LLMs) have demonstrated remarkable capabilities in generating programs from natural language descriptions, yet ensuring their correctness without an external…
Hidden Reliability Risks in Large Language Models: Systematic Identification of Precision-Induced Output Disagreements
Yifei Wang, Tianlin Li, Xiaohan Zhang +4
Large language models (LLMs) are increasingly deployed under diverse numerical precision configurations, including standard floating-point formats (e.g., bfloat16 and float16) and…
AutoEG: Exploiting Known Third-Party Vulnerabilities in Black-Box Web Applications
Ruozhao Yang, Mingfei Cheng, Gelei Deng +3
Large-scale web applications are widely deployed with complex third-party components, inheriting security risks arising from component vulnerabilities. Security assessment is there…
PentestEval: Benchmarking LLM-based Penetration Testing with Modular and Stage-Level Design
Ruozhao Yang, Mingfei Cheng, Gelei Deng +3
Penetration testing is essential for assessing and strengthening system security against real-world threats, yet traditional workflows remain highly manual, expertise-intensive, an…
STCLocker: Deadlock Avoidance Testing for Autonomous Driving Systems
Mingfei Cheng, Renzhi Wang, Xiaofei Xie +2
Autonomous Driving System (ADS) testing is essential to ensure the safety and reliability of autonomous vehicles (AVs) before deployment. However, existing techniques primarily foc…
ADReFT: Adaptive Decision Repair for Safe Autonomous Driving via Reinforcement Fine-Tuning
Mingfei Cheng, Xiaofei Xie, Renzhi Wang +2
Autonomous Driving Systems (ADSs) continue to face safety-critical risks due to the inherent limitations in their design and performance capabilities. Online repair plays a crucial…
Foundation Models for Autonomous Driving System: An Initial Roadmap
Xiongfei Wu, Mingfei Cheng, Xiaoning Ren +8
Recent advances in foundation models (FMs), including large language models (LLMs), vision-language models (VLMs), and world models, have opened new opportunities for autonomous dr…
MoDitector: Module-Directed Testing for Autonomous Driving Systems
Renzhi Wang, Mingfei Cheng, Xiaofei Xie +2
Testing Autonomous Driving Systems (ADS) is crucial for ensuring their safety, reliability, and performance. Despite numerous testing methods available that can generate diverse an…
DriveTester: A Unified Platform for Simulation-Based Autonomous Driving Testing
Mingfei Cheng, Yuan Zhou, Xiaofei Xie
Simulation-based testing plays a critical role in evaluating the safety and reliability of autonomous driving systems (ADSs). However, one of the key challenges in ADS testing is t…
LMM-enhanced Safety-Critical Scenario Generation for Autonomous Driving System Testing From Non-Accident Traffic Videos
Haoxiang Tian, Xingshuo Han, Yuan Zhou +6
Safety testing serves as the fundamental pillar for the development of autonomous driving systems (ADSs). To ensure the safety of ADSs, it is paramount to generate a diverse range…
ContrastRepair: Enhancing Conversation-Based Automated Program Repair via Contrastive Test Case Pairs
Jiaolong Kong, Mingfei Cheng, Xiaofei Xie +3
Automated Program Repair (APR) aims to automatically generate patches for rectifying software bugs. Recent strides in Large Language Models (LLM), such as ChatGPT, have yielded enc…
Decictor: Towards Evaluating the Robustness of Decision-Making in Autonomous Driving Systems
Mingfei Cheng, Yuan Zhou, Xiaofei Xie +3
Autonomous Driving System (ADS) testing is crucial in ADS development, with the current primary focus being on safety. However, the evaluation of non-safety-critical performance, p…