15 papers
Test-Driven Approaches to Software Engineering with Large Language Models: A Survey of Phases, Tasks, and Agent Skills
Yunhao Liang, Chengguang Gan, Ruixuan Ying +3
Tests increasingly participate in the decisions made by large language models and software engineering agents. They specify intended behavior, guide program construction and repair…
Correct Tests Are Not Enough: Measuring and Training Oracle Conversion in Specification-Based Test Generation
Yunhao Liang, Chengguang Gan, Ruixuan Ying +3
Generating tests from a natural-language specification requires both an input that exposes faulty behavior and a correct expected output. These requirements need not improve togeth…
How Output Format Confounds Data Quality and Capability in Instruction Tuning
Chengguang Gan, Hanjun Wei, Yunhao Liang +3
Instruction-tuning data are judged by quality metrics, and tuned models are judged by benchmarks, but both judgments pass through an output interface: the surface format in which a…
Joint Training Is Not Enough: Conditioned Cross-Granularity Training for Multimodal Document Understanding
Chengguang Gan, Yunhao Liang, Hanjun Wei +2
The Mutual Reinforcement Effect (MRE) asks whether a fine, span-level and a coarse, document-level task help each other when one model handles both. We test it in multimodal docume…
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…