CT-Repair: Multi-Perspective Agentic Program Repair via Code Property Graphs and Temporal Execution Graphs
Score
81提出了CPG+TEG+多智能体联合修复的新范式，并给出了详细的压缩率与修复效果对比，信息增量显著。
方法描述清晰，实验设计严谨（854错误、多基线、消融分析），数据与图表支撑充分。
在真实缺陷数据集上取得超越现有系统的效果，且压缩技术可直接降低LLM调用成本，具有较强工程价值。
论文于2026年7月发布，是最新的APR研究成果，紧跟LLM与软件工程结合的前沿。
arXiv预印本，来自知名学术机构，含完整代码与数据链接，可信度高。
Base 89.5 × T2 Source weight ×0.9 = 81
Curation line 70Selected
Why selected
The paper achieves top-tier standards in both methodology design (CPG+TEG+multi-agent) and experimental validation (854 bugs, multiple baselines, ablation analysis), with rich data and strong novelty, significantly advancing the field of automated program repair.
AI commentary
「What impresses me most about CT-Repair is its philosophy: 'Don't just increase the number of patches, let the model see the soul of the program'—by using CPG and TEG to let LLMs understand the static skeleton and dynamic lineage of code. For researchers and engineers in AI for Software Engineering, this paper offers not only a reproducible high-performance baseline but also a clever integration of program analysis tools with LLMs, especially valuable in context-window-sensitive scenarios. I highly recommend reproducing the experiments with their open-source toolchain.」
Quick overview
Large language models (LLMs) have improved automated program repair (APR), but face limitations such as repetitive execution traces and lack of root-cause diversity in patch sampling. This paper presents CT-Repair, an agentic APR framework that represents static and dynamic evidence as queryable Code Property Graphs (CPG) and Temporal Execution Graphs (TEG). A three-stage filtering pipeline constructs compact TEGs, and three finite-state-machine-guided agents analyze each bug from static, dynamic, and hybrid perspectives to produce evidence-grounded repair strategies. Strategy-guided generation instantiates these strategies as candidate patches and refines the most promising one using validation feedback. Evaluated on 854 Java bugs from Defects4J v3.0, CT-Repair correctly repairs 489 bugs in a mixed-model configuration, and under a controlled GPT-5.4-mini configuration, it repairs 388 bugs, surpassing ReinFix and RepairAgent by 19 and 30 respectively. The union of the three perspectives repairs 99 more bugs than the strongest individual perspective. The filtering pipeline compacts runtime evidence, with execution filtering narrowing candidate method scope by 94.85% on average and behavior filtering further reducing retained runtime records by 55.97%. These results demonstrate that structured runtime evidence and multi-perspective reasoning can improve repair effectiveness without solely relying on a larger patch-generation budget.
- CT-Repair unifies static and dynamic program evidence via Code Property Graphs (CPG) and Temporal Execution Graphs (TEG), enabling multi-perspective reasoning.
- A three-stage filtering pipeline (execution filtering, behavior filtering, TEG construction) compresses runtime evidence to under 5% of its original size, drastically improving context efficiency.
- Three finite-state-machine-guided agents generate repair strategies from static, dynamic, and hybrid perspectives; strategy-directed patch generation is refined via validation feedback.
- On Defects4J v3.0, CT-Repair with GPT-5.4-mini correctly repairs 388 bugs, surpassing ReinFix and RepairAgent, and the union of perspectives yields 99 more fixes than the strongest single perspective.
Full text
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