Multi-Perspective Agentic Program Repair via Code Property Graphs and Temporal Execution Graphs

2026-07-14

AI Summary

This work addresses the limited diversity in repair strategies generated by current large language models for automated program repair, which often stems from redundant execution traces and repetitive sampling. To overcome this, the authors propose CT-Repair, a novel framework that integrates static and dynamic evidence by combining Code Property Graphs (CPGs) with Temporal Execution Graphs (TEGs). CT-Repair introduces a finite state machine–guided multi-perspective agent collaboration mechanism, enabling independent generation and optimization of diverse repair strategies. Coupled with a three-stage filtering pipeline and validation feedback, the approach substantially enhances both repair diversity and accuracy. Evaluated on 854 Java bugs from Defects4J v3.0, CT-Repair successfully repairs 489, outperforming ReinFix and RepairAgent; the joint use of three perspectives yields 99 more fixes than the strongest single perspective, while execution-based filtering reduces the search space by an average of 94.85%.

Abstract

Large language models (LLMs) have improved automated program repair (APR), but two limitations remain. First, raw execution traces are often too large and repetitive to serve as effective model context. Second, repeated patch sampling may produce different implementations without yielding distinct root-cause hypotheses or repair strategies. We present CT-Repair, an agentic APR framework representing static and dynamic evidence as queryable Code Property Graph (CPG) and Temporal Execution Graph (TEG). CT-Repair applies a three-stage filtering pipeline to construct compact TEGs. Three finite-state-machine-guided agents analyze each bug from static, dynamic, and hybrid perspectives and independently produce evidence-grounded repair strategies. A strategy-guided generation procedure instantiates these strategies as candidate patches and uses validation feedback to refine the most promising strategy. We evaluate CT-Repair on 854 Java bugs from Defects4J v3.0. In the mixed-model configuration, CT-Repair correctly repairs 489 bugs. Under a controlled GPT-5.4-mini configuration, it repairs 388 bugs, 19 and 30 more than ReinFix and RepairAgent, respectively. The union of the three evidence perspectives repairs 99 more bugs than the strongest individual perspective. The filtering pipeline also compacts runtime evidence, with execution filtering narrowing the candidate method scope by 94.85% on average and behavior filtering further reducing retained runtime records by 55.97%. These results show that structured runtime evidence and multi-perspective reasoning can improve repair effectiveness without relying solely on a larger patch-generation budget.

Problem

• Automated Program Repair
• Execution Traces
• Patch Sampling
• Root-Cause Hypotheses
• Repair Strategies

Innovation

• Code Property Graph
• Temporal Execution Graph
• Multi-Perspective Reasoning
• Agentic Program Repair
• Execution Trace Filtering