From the 1 of 7 linked papers with an AI index.
7 papers
Auditing and Decomposing Feedback-Driven Evolution in LLM Test Generation under the Oracle Problem
Yunhao Liang, Chengguang Gan, Ruixuan Ying +3
Execution feedback is often treated as a self-verifying signal for improving LLM-generated tests. However, when generated inputs are executed on a single accepted program and its o…
Security Tests as Executable Specifications for LLM Code Generation: Benefits, Trade-offs, and Coverage Limits
Yunhao Liang, Chengguang Gan, Ruixuan Ying +3
Large language models (LLMs) can generate functionally useful code that remains vulnerable, while security-focused interventions may break intended behavior. We investigate securit…
Do Code Language Models Use Tests? A Behavioral and Representational Study of Test-Driven Code Generation
Yunhao Liang, Chengguang Gan, Ruixuan Ying +3
The paper investigates how code language models respond to test cases in prompts, analyzing whether tests act as executable specifications or merely extra context, and finds that t…
Scaling Test-Driven Code Generation from Functions to Classes: An Empirical Study
Yunhao Liang, Ruixuan Ying, Shiwen Ni +1
Test-driven development (TDD) has been adopted to improve Large Language Model (LLM)-based code generation by using tests as executable specifications. However, existing TDD-style…
Visual Merit or Linguistic Crutch? A Close Look at DeepSeek-OCR
Yunhao Liang, Ruixuan Ying, Bo Li +7
DeepSeek-OCR utilizes an optical 2D mapping approach to achieve high-ratio vision-text compression, claiming to decode text tokens exceeding ten times the input visual tokens. Whil…
HyClone: Bridging LLM Understanding and Dynamic Execution for Semantic Code Clone Detection
Yunhao Liang, Ruixuan Ying, Takuya Taniguchi +2
Code clone detection is a critical task in software engineering, aimed at identifying duplicated or similar code fragments within or across software systems. Traditional methods of…