用LLM合成训练数据，破解自动修Bug的数据瓶颈，思路新颖且实战价值高。
arXiv:2505.07372v3 Announce Type: replace-cross Abstract: This paper presents a novel methodology for enhancing Automated Program Repair (APR) through…
不只是修bug，这篇研究重新审视了缺陷复杂度与定位策略，用成本效率视角重构自动化修复的边界。
arXiv:2608.14065v1 Announce Type: cross Abstract: Background: Software bugs remain a critical challenge in development, necessitating effective Automa…
把代码修复变成“看图找茬”，用结构化视觉推理定位缺陷，AI编程新思路。
arXiv:2602.06090v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have recently been applied to Automated Program Repair (APR), y…
破解多轮程序修复难题，迭代优化修复指令，让AI纠错不再“越修越坏”
arXiv:2604.23989v2 Announce Type: replace-cross Abstract: Recent work on large language models (LLMs) has emphasized the importance of scaling inferen…
可视化AI智能体修复代码的完整轨迹，揭示其决策过程与修复路径。
arXiv:2606.22110v1 Announce Type: cross Abstract: LLM-based automated program repair (APR) agents generate patches to fix software bugs with minimal h…
ISSTA '26收录的实证研究，首次从可追溯性视角系统评估自动化程序修复智能体的行为与效果。
arXiv:2506.08311v2 Announce Type: replace-cross Abstract: Automated Program Repair (APR) agents leverage Large Language Models (LLMs) to autonomously …