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arxiv: 2606.22110 · v1 · pith:GNTRTQCEnew · submitted 2026-06-20 · 💻 cs.SE · cs.AI· cs.HC

TraceView: Interactive Visualization of Agentic Program Repair Trajectories

Amirali Sajadi , Tu Nguyen , Kimmie Huynh , Esteban Parra , Preetha Chatterjee This is my paper

Pith reviewed 2026-06-26 11:33 UTC · model grok-4.3

classification 💻 cs.SE cs.AIcs.HC
keywords automated program repairLLM agentstrajectory visualizationinteractive debuggingagent behavior analysissoftware engineering tools
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The pith

TraceView structures agent repair trajectories into labeled Thought-Action-Result graphs to diagnose how LLM patches are generated.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

LLM-based automated program repair agents produce long trajectories of reasoning, tool calls, and feedback, but final patch outcomes alone do not reveal where the process went wrong or became repetitive. TraceView organizes these raw runs into Thought, Action, and Result components, supports semantic relation labeling, and renders them as interactive graph views with filters, summaries, metrics, and evidence panels. A survey with five researchers showed that the overview-to-detail workflow made trajectories easier to scan and helped users understand repair behavior. The tool targets the gap between visible outcomes and the hidden steps that determine success or failure in agentic repair.

Core claim

TraceView organizes raw and pre-labeled agentic runs with Thought, Action, and Result components to support semantic relation labeling and diagnosis, and renders the resulting trajectory as graph views with relation filters, patch outcome summaries, metrics, and node-level evidence panels.

What carries the argument

The graph view of Thought-Action-Result components that enables labeling, filtering by relations, and inspection of connections across repair steps.

If this is right

  • Users can identify repetitive or misaligned steps that cause repair failures.
  • Relation labeling makes it possible to compare behavior across multiple repair attempts.
  • Overview-to-detail navigation supports quicker diagnosis than raw logs.
  • Metrics and outcome summaries highlight patterns in successful versus failed runs.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same component labeling and graph rendering could apply to agent trajectories in other domains such as web navigation or code generation.
  • Collected labels might serve as training signals to reduce misalignment in future agent designs.
  • Integration into APR pipelines could shift evaluation from patch success alone to process-level analysis.

Load-bearing premise

Raw agent trajectories contain semantically meaningful Thought-Action-Result components that users can reliably label to support diagnosis.

What would settle it

A study in which participants using TraceView show no measurable improvement in identifying failure points or understanding repair steps compared to viewing unprocessed trajectory logs.

Figures

Figures reproduced from arXiv: 2606.22110 by Amirali Sajadi, Esteban Parra, Kimmie Huynh, Preetha Chatterjee, Tu Nguyen.

Figure 1
Figure 1. Figure 1: TraceView provides two graph views for APR trajectory analysis. The iteration view summarizes each step by action category, while the detailed [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: TraceView’s labeling workspace lets users assign action categories and inspect source evidence before labeling semantic relations. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
read the original 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 repair failures difficult to diagnose, reproduce, and prevent. To help developers address these challenges, we present TraceView, an interactive tool for labeling and visualizing repair trajectories from APR systems. TraceView organizes raw and pre-labeled agentic runs with Thought, Action, and Result components to support semantic relation labeling and diagnosis, and renders the resulting trajectory as graph views. Furthermore, TraceView provides relation filters, patch outcome summaries, metrics, and node-level evidence panels to help users inspect how reasoning, actions, and feedback connect across the various steps of an agentic repair attempt. We evaluate TraceView with five researchers through a survey-based user study. Participants reported that TraceView made trajectories easier to scan and that its overview-to-detail workflow helped them better understand repair behavior. The TraceView source code is available at https://github.com/SOAR-Lab/agent-traj-visualization. A screencast of TraceView is available at https://youtu.be/9ZCh7Ifj2AQ.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper presents TraceView, an interactive tool for labeling and visualizing trajectories from LLM-based automated program repair (APR) agents. It organizes raw and pre-labeled runs into Thought-Action-Result components, renders them as graph views with relation filters, patch outcome summaries, metrics, and node-level evidence panels, and evaluates the tool via a survey-based user study with five researchers. Participants reported that TraceView made trajectories easier to scan and that its overview-to-detail workflow helped them better understand repair behavior. Source code and a screencast are provided.

Significance. If the reported benefits hold, TraceView addresses a practical gap in diagnosing failures in agentic APR by making long reasoning trajectories more interpretable beyond final patch outcomes. The open-source release supports reproducibility and extension within the software engineering community.

major comments (1)
  1. [Evaluation] Evaluation section: the usability claims rest on qualitative self-reports from only five participants with no quantitative metrics (e.g., task completion time, error rates), no baseline comparison to existing visualization tools, and no details on survey questions or participant demographics; while the central claim is scoped to what participants reported, this design limits the strength of evidence for the overview-to-detail workflow benefit.
minor comments (2)
  1. [Abstract / Introduction] Abstract and §1: the description of how Thought-Action-Result components are obtained (manual labeling, automated extraction, or both) is not fully specified, which affects readers' understanding of the tool's input requirements.
  2. [Figures / Implementation] Figure captions and §3: ensure all graph view screenshots include explicit legends for node/edge types and filter controls to improve standalone readability.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and the recommendation for minor revision. We address the evaluation comment below by clarifying the study scope and offering to enhance transparency.

read point-by-point responses

  1. Referee: [Evaluation] Evaluation section: the usability claims rest on qualitative self-reports from only five participants with no quantitative metrics (e.g., task completion time, error rates), no baseline comparison to existing visualization tools, and no details on survey questions or participant demographics; while the central claim is scoped to what participants reported, this design limits the strength of evidence for the overview-to-detail workflow benefit.

    Authors: We acknowledge the limitations of the evaluation design. The user study was intentionally scoped as an initial, exploratory assessment with five researchers to gather qualitative feedback on whether TraceView improves scanability and understanding of trajectories, as explicitly stated in the abstract and evaluation section. Quantitative metrics, task timings, error rates, and comparisons to baseline tools were not collected because the primary aim was to validate the core visualization concepts (Thought-Action-Result organization and overview-to-detail workflow) rather than to perform a controlled comparative usability experiment. We agree that this limits the strength of evidence for broader claims. To improve transparency, we will add the survey questions, participant demographics, and background details in a revision. A larger quantitative study with baselines would require substantial new work outside the current scope. revision: partial

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper presents a visualization tool and reports direct results from a five-participant survey on usability. No mathematical derivations, predictions, fitted parameters, or uniqueness theorems appear anywhere in the text. The central claim is a factual summary of self-reported participant feedback on scanning ease and workflow helpfulness; it does not reduce to any input by construction, self-citation chain, or ansatz. The description of Thought-Action-Result organization is presented as a design choice of the tool rather than a derived result. This is a standard non-circular tool-description paper whose claims are scoped exactly to the reported user responses.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, mathematical axioms, or invented entities are introduced; the contribution is a software tool whose core assumptions are domain conventions about trajectory structure rather than new postulates.

pith-pipeline@v0.9.1-grok · 5795 in / 1090 out tokens · 19487 ms · 2026-06-26T11:33:16.467745+00:00 · methodology

discussion (0)

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