论文

已发表与在投工作的本地 PDF 归档。

  • To Run or Not to Run: Analyzing the Cost-Effectiveness of Code Execution in LLM-Based Program Repair

    PDF

    ISSTA 2026

    #Program Repair #Code Agents

  • How Much Static Structure Do Code Agents Need? A Study of Deterministic Anchoring

    PDF

    ISSTA 2026

    #Static Analysis #Code Agents

  • Phantom Rendering Detection: Identifying and Analyzing Unnecessary UI Computations

    PDF

    FSE 2026

    #Performance

  • MazeBreaker: Multi-Agent Reinforcement Learning for Dynamic Jailbreaking of LLM Security Defenses

    PDF

    ICSE 2026

    #LLM Security

  • HapRepair: Learn to Repair OpenHarmony Apps

    PDF

    FSE Industry 2025

    #Program Repair #Static Analysis

  • Effective Fine-tuning for Low-resource Languages: A Case Study of Cangjie

    PDF

    EMSE 2026

    #Misc

  • Open-Source AI-based SE Tools: Opportunities and Challenges of Collaborative Software Learning

    PDF

    TOSEM 2024

    #Survey

  • Exploring Code Analysis: Zero-Shot Insights on Syntax and Semantics with LLMs

    PDF

    TOSEM 2026

    #Static Analysis

  • OpenHarmony Bench: Evaluating LLMs and Coding Agents on OpenHarmony App Development

    预印本

    Technical Report 2026

    An app-level benchmark for OpenHarmony ArkTS development that evaluates whether coding agents can turn feature requests, structured specifications, and bug reports into buildable applications with correct end-to-end behavior.

  • RepoRescue: An Empirical Study of LLM Agents on Whole-Repository Compatibility Rescue

    预印本

    Studies whether deployed LLM agents can rescue old repositories broken by runtime and dependency drift, with source-only auditing, runtime blocking, and realistic-use validation.

  • AtomicCommitBench: Can Coding Agents Reconstruct Commit Histories from Squashed Patches?

    预印本

    Benchmarks whether coding agents can reconstruct replayable, reviewable commit histories from squashed patches, measuring replay validity, human-aligned grouping, and selective-revert failure containment.

  • Executing as You Generate: Hiding Execution Latency in LLM Code Generation

    预印本

    Proposes EAGER, a parallel execution paradigm for LLM code generation that overlaps code generation and execution, reducing end-to-end latency by up to 55%.