TraceView: Interactive Visualization of Agentic Program Repair Trajectories

📰 ArXiv cs.AI

Learn to visualize agentic program repair trajectories with TraceView, enhancing understanding of LLM-based automated program repair agents

advanced Published 23 Jun 2026

Action Steps

Build an interactive visualization of agentic program repair trajectories using TraceView
Run TraceView on a dataset of repair attempts to identify patterns and areas for improvement
Configure TraceView to highlight repetitive or misaligned processes
Test the effectiveness of TraceView in improving repair outcomes
Apply TraceView to real-world program repair scenarios to enhance agent performance

Who Needs to Know This

Developers and researchers working with LLM-based automated program repair agents can benefit from TraceView to improve the efficiency and effectiveness of their repair processes

Key Insight

💡 Visualizing agentic program repair trajectories can help identify areas for improvement and increase the efficiency of LLM-based automated program repair agents

Full Article

Title: TraceView: Interactive Visualization of Agentic Program Repair Trajectories

Abstract:
arXiv:2606.22110v1 Announce Type: cross Abstract: LLM-based automated program repair (APR) agents generate patches to fix software bugs with minimal human intervention. These agents often produce long trajectories of reasoning, tool use, and feedback to produce candidate patches. Final patch outcomes show whether a repair attempt succeeded or failed, but they do not show how the agent reached that outcome, or where the process became repetitive or misaligned with the task. This makes agentic rep

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