activity
20242026
most citedOBsmith: LLM-Powered JavaScript Obfuscator Testing

2 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.SE2026

Semantic Voting: Execution-Grounded Consensus for LLM Code Generation

Shan Jiang, Zijian Yi, Chenguang Zhu

LLM code-generation pipelines often sample multiple candidates and select one final answer without access to a complete oracle. Existing pipelines mix textual voting, ranking, and…

cs.LG2026

Sketch-and-Verify: Structured Inference-Time Scaling via Program Sketching

Shan Jiang, Zijian Yi, Chenguang Zhu

SKETCHVERIFY is a within-tier cost-performance policy, not a universal accuracy improvement. The operational question: a practitioner stuck with a small, cheap code model (here, Ge…

cs.SE20252 cited

OBsmith: LLM-Powered JavaScript Obfuscator Testing

Shan Jiang, Chenguang Zhu, Sarfraz Khurshid

JavaScript obfuscators are widely deployed to protect intellectual property and resist reverse engineering, yet their correctness has been largely overlooked compared to performanc…

cs.SE2024

Generating executable oracles to check conformance of client code to requirements of JDK Javadocs using LLMs

Shan Jiang, Chenguang Zhu, Sarfraz Khurshid

Software testing remains the most widely used methodology for validating quality of code. However, effectiveness of testing critically depends on the quality of test suites used. T…

cs.SE2024

Detecting Multi-Parameter Constraint Inconsistencies in Python Data Science Libraries

Xiufeng Xu, Fuman Xie, Chenguang Zhu +3

Modern AI- and Data-intensive software systems rely heavily on data science and machine learning libraries that provide essential algorithmic implementations and computational fram…