2 citations · 5 across the 11 of their papers we have counts for
12 papers
Aletheia: Permission-Minimality Testing for Coding-Agent Rules
Jieke Shi, Yuchen Chen, Junda He +2
Coding agents powered by Large Language Models (LLMs) read repository rules (e.g., AGENTS.md and Cursor rules) to learn project conventions and development procedures. An attacker…
Second Thought: Reasoning in Parallel as LLM Agents Act and Observe
Zhensu Sun, Chengran Yang, Yunbo Lyu +2
LLM agents in the ReAct paradigm alternate between reasoning, acting, and observing, but deliberate reasoning is confined to the Thought phase: while the agent serializes an action…
Beyond Text Matching: Towards Reference-Free Evaluation for Human-Oriented Binary Reverse Engineering
Xiuwei Shang, Li Hu, Xiao Jiang +7
Human-Oriented Binary Reverse Engineering (HOBRE) aims to transform decompiled pseudocode into a more human-friendly representation, thereby reducing the cognitive burden of revers…
Lossless Tensor Compression as Program Synthesis
Jieke Shi, Junda He, Wenjia Jiang +11
Model checkpoints are growing in both number and size, which makes archival, transfer, and deployment increasingly costly. General-purpose compressors can reduce storage requiremen…
Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation
Li Hu, Xiuwei Shang, Jieke Shi +6
Deobfuscating binary code remains a fundamental challenge in reverse engineering, as obfuscation is widely used to hinder analysis and conceal program logic. Although large languag…
AgentSZZ: Teaching the LLM Agent to Play Detective with Bug-Inducing Commits
Yunbo Lyu, Jieke Shi, Hong Jin Kang +8
The SZZ algorithm is the dominant technique for identifying bug-inducing commits and underpins many software engineering tasks, such as defect prediction and vulnerability analysis…
Compiling Code LLMs into Lightweight Executables
Jieke Shi, Junda He, Zhou Yang +6
The demand for better prediction accuracy and higher execution performance in neural networks continues to grow. The emergence and success of Large Language Models (LLMs) have prod…
Finding Memory Leaks in C/C++ Programs via Neuro-Symbolic Augmented Static Analysis
Huihui Huang, Jieke Shi, Bo Wang +2
Memory leaks remain prevalent in real-world C/C++ software. Static analyzers such as CodeQL provide scalable program analysis but frequently miss such bugs because they cannot reco…
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…
"My productivity is boosted, but ..." Demystifying Users' Perception on AI Coding Assistants
Yunbo Lyu, Zhou Yang, Jieke Shi +3
This paper aims to explore fundamental questions in the era when AI coding assistants like GitHub Copilot are widely adopted: what do developers truly value and criticize in AI cod…
SLICEMATE: Accurate and Scalable Static Program Slicing via LLM-Powered Agents
Jianming Chang, Jieke Shi, Yunbo Lyu +5
Static program slicing, which extracts the executable portions of a program that affect the values at a specific location, supports many software analysis tasks such as debugging a…
Mut4All: Fuzzing Compilers via LLM-Synthesized Mutators Learned from Bug Reports
Bo Wang, Pengyang Wang, Chong Chen +9
Mutation-based fuzzing is effective for uncovering compiler bugs, but designing high-quality mutators for modern languages with complex constructs (e.g., templates, macros) remains…