collaborators

10 papers

cs.SE2026

CausalRepair: Bridging the Causality Gap in Large Language Model-Based Automated Program Repair via Dual-Slicing

Linhao Wu, Yizhou Chen, Zhen Yang +2

Automated Program Repair (APR) has recently benefited from Large Language Models (LLMs), yet their effectiveness heavily depends on repair context. Existing LLM-based APR methods s…

cs.SE2026

Porting Declarative UI to HarmonyOS: A Heuristic-guided LLM Approach

Kunwu Zheng, Pengyu Xue, Zhen Yang +8

As an emerging operating system, HarmonyOS has a significant demand for software migration from platforms such as Android and iOS, where the User Interface (UI) translation account…

cs.SE2026

DebugRepair: Enhancing LLM-Based Automated Program Repair via Self-Directed Debugging

Linhao Wu, Yifei Pei, Zhen Yang +9

Automated Program Repair (APR) has benefited from the code understanding and generation capabilities of Large Language Models (LLMs). Existing feedback-based APR methods iterativel…

cs.SE2026

Persistent Cross-Attempt State Optimization for Repository-Level Code Generation

Ruwei Pan, Jiangshuai Wang, Qisheng Zhang +6

Large language models (LLMs) have achieved substantial progress in repository-level code generation. However, solving the same repository-level task often requires multiple attempt…

cs.SE2026

Toward Executable Repository-Level Code Generation via Environment Alignment

Ruwei Pan, Junlei Shen, Linhao Wu +5

Large language models (LLMs) have achieved strong performance on code generation, but existing methods still struggle with repository-level code generation under executable validat…

cs.SE2026

Fixturize: Bridging the Fixture Gap in Test Generation

Chengyi Wang, Pengyu Xue, Zhen Yang +8

Current Large Language Models (LLMs) have advanced automated unit test generation but face a critical limitation: they often neglect to construct the necessary test fixtures, which…