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20232026
most citedSymbol Preference Aware Generative Models for Recovering Variable Names from Stripped Binary

4 citations · 18 across the 22 of their papers we have counts for

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12 papers · 1 filter

cs.SE2026

WitnessGym: Benchmarking Coding Agents on the Construction of Bug Witnesses

Haomin Qi, Xiangzhe Xu, Yiming Huang +2

Bug validation asks a coding agent to produce an executable witness for a reported bug. The witness combines a concrete input with a testing harness and exposes faulty behavior dur…

cs.SE2026

Natural-Language Workflows Are Not Software Yet: Artifact-Driven Compilation for Reliable Agent Execution

Xiangzhe Xu, Hanxi Guo, Guangyu Shen +2

Natural-language workflows offer a software-like interface for agents: domain experts can write reusable procedures, and agents can execute them as instructions. This promise is no…

cs.SE2026

The Devil Is in the Interface: Evaluating How Tool Architecture Shapes Coding Agent Behavior

Xiangzhe Xu, Hamidreza Saghir, Qianhui Wu +5

As large language models continue to improve, agentic systems are becoming increasingly important, and tools are a key design dimension because they determine how agents access inf…

cs.SE2025

Position: Intelligent Coding Systems Should Write Programs with Justifications

Xiangzhe Xu, Shiwei Feng, Zian Su +2

Intelligent coding systems are transforming software development by enabling users to specify code behavior in natural language. However, the opaque decision-making of AI-driven co…

cs.SE2025

TAI3: Testing Agent Integrity in Interpreting User Intent

Shiwei Feng, Xiangzhe Xu, Xuan Chen +5

LLM agents are increasingly deployed to automate real-world tasks by invoking APIs through natural language instructions. While powerful, they often suffer from misinterpretation o…

cs.SE2025★ 3 cited

RepoAudit: An Autonomous LLM-Agent for Repository-Level Code Auditing

Jinyao Guo, Chengpeng Wang, Xiangzhe Xu +2

Code auditing is the process of reviewing code with the aim of identifying bugs. Large Language Models (LLMs) have demonstrated promising capabilities for this task without requiri…

cs.SE2024

ROCAS: Root Cause Analysis of Autonomous Driving Accidents via Cyber-Physical Co-mutation

Shiwei Feng, Yapeng Ye, Qingkai Shi +5

As Autonomous driving systems (ADS) have transformed our daily life, safety of ADS is of growing significance. While various testing approaches have emerged to enhance the ADS reli…

cs.SE2024

Source Code Foundation Models are Transferable Binary Analysis Knowledge Bases

Zian Su, Xiangzhe Xu, Ziyang Huang +2

Human-Oriented Binary Reverse Engineering (HOBRE) lies at the intersection of binary and source code, aiming to lift binary code to human-readable content relevant to source code,…

cs.SE2024

CodeArt: Better Code Models by Attention Regularization When Symbols Are Lacking

Zian Su, Xiangzhe Xu, Ziyang Huang +4

Transformer based code models have impressive performance in many software engineering tasks. However, their effectiveness degrades when symbols are missing or not informative. The…

cs.SE2023★ 1 cited

Nova: Generative Language Models for Assembly Code with Hierarchical Attention and Contrastive Learning

Nan Jiang, Chengxiao Wang, Kevin Liu +4

Binary code analysis is the foundation of crucial tasks in the security domain; thus building effective binary analysis techniques is more important than ever. Large language model…

cs.SE2023

PEM: Representing Binary Program Semantics for Similarity Analysis via a Probabilistic Execution Model

Xiangzhe Xu, Zhou Xuan, Shiwei Feng +7

Binary similarity analysis determines if two binary executables are from the same source program. Existing techniques leverage static and dynamic program features and may utilize a…

cs.SE2023★ 4 cited

Symbol Preference Aware Generative Models for Recovering Variable Names from Stripped Binary

Xiangzhe Xu, Zhuo Zhang, Zian Su +8

Decompilation aims to recover the source code form of a binary executable. It has many security applications, such as malware analysis, vulnerability detection, and code hardening.…