10 papers
LLM-Based Test Generation: Information Sources, Generation Strategies, and Quality Evidence
Yunhao Liang, Chengguang Gan, Ruixuan Ying +3
Large language models are increasingly used to generate test scenarios, executable test suites, assertions, interaction sequences, and fuzzing infrastructure. These artifacts serve…
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…
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 Follow Tests? Paired Interventions on Program Behavior
Yunhao Liang, Chengguang Gan, Ruixuan Ying +3
Visible tests specify concrete program behavior, but an improvement in benchmark accuracy does not establish that a model follows the rule expressed by those tests. We study test u…
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…
Failures Are the Stepping Stones to Success: Enhancing Few-Shot In-Context Learning by Leveraging Negative Samples
Yunhao Liang, Ruixuan Ying, Takuya Taniguchi +1
Large Language Models exhibit powerful few-shot in-context learning (ICL) capabilities, but the performance is highly sensitive to provided examples. Recent research has focused on…