1 citations · 1 across the 13 of their papers we have counts for
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Trustworthy Runtime Error Healing in Real-World Repositories: A Benchmark and Guardrail
Gou Tan, Pengfei Chen, Zhensu Sun +9
Runtime error healing lets a crashed program continue by generating code that repairs its live runtime state. Recent work shows that LLMs can generate such healing code, but it is…
Path2Spec: Path-Aware Specification Generation via Large Language Models
Dan Huang, Zhensu Sun, Huihui Huang +4
Formal specifications are critical for program verification, comprehension, and maintenance. However, manually writing them is costly and difficult to scale. Recent studies have sh…
AgentChaos: Chaos Engineering for Agent Systems via Programmatic Fault Injection
Gou Tan, Zhensu Sun, Jieke Shi +10
Agent systems rely on LLM APIs for every response, but these APIs can return server errors, truncated responses, or corrupted content that propagates through downstream agents and…
Automated Repair of TEE Partitioning Issues via DSL-Guided and LLM-Assisted Patching
Chengyan Ma, Jieke Shi, Ruidong Han +4
Trusted Execution Environments (TEEs) provide hardware-based isolation to protect sensitive data and computations from potentially compromised operating systems (OS). However, TEE…
Finding Missing Input Validation in TEEs via LLM-Assisted Symbolic Execution
Chengyan Ma, Jieke Shi, Ruidong Han +3
Trusted Execution Environments (TEEs) provide hardware-enforced isolation that protects sensitive code and data from untrusted software. Despite their strong security guarantees, a…
Beyond the Tip of the Iceberg: Understanding SATD in Dockerfiles through the Lens of Co-evolution
Wei Minn, Yan Naing Tun, Biniam Fesseha Demissie +5
Dockerfiles enable the creation of portable container-based execution environments for the application code, and have become an important part of the modern software development pr…
An Execution-Verified Multi-Language Benchmark for Code Semantic Reasoning
Yikun Li, Jinfeng Jiang, Ting Zhang +7
Evaluating whether large language models (LLMs) can recover execution-relevant program structure, rather than only produce code that passes tests, remains an open problem. Existing…
Revisiting Vulnerability Patch Identification on Data in the Wild
Ivana Clairine Irsan, Ratnadira Widyasari, Ting Zhang +7
Attacks can exploit zero-day or one-day vulnerabilities that are not publicly disclosed. To detect these vulnerabilities, security researchers monitor development activities in ope…
Autoregressive, Yet Revisable: In Decoding Revision for Secure Code Generation
Chengran Yang, Zichao Wei, Heminghao Deng +6
Large Language Model (LLM) based code generation is predominantly formulated as a strictly monotonic process, appending tokens linearly to an immutable prefix. This formulation con…
PenForge: On-the-Fly Expert Agent Construction for Automated Penetration Testing
Huihui Huang, Jieke Shi, Junkai Chen +6
Penetration testing is essential for identifying vulnerabilities in web applications before real adversaries can exploit them. Recent work has explored automating this process with…
LIDL: LLM Integration Defect Localization via Knowledge Graph-Enhanced Multi-Agent Analysis
Gou Tan, Zilong He, Min Li +8
LLM-integrated software, which embeds or interacts with large language models (LLMs) as functional components, exhibits probabilistic and context-dependent behaviors that fundament…