data/processed/README.md

AI-NativeBench: Processed Results & Analysis Artifacts

This directory contains processed experiment outputs, aggregated statistics, and analysis/visualization scripts for the paper "AI-NativeBench: An Open-Source White-Box Agentic Benchmark Suite for AI-Native Systems".

Compared with the raw dataset (see README-RAW.md), this RESULTS/ folder additionally includes:

• Derived artifacts such as execution_path.md (extracted trace trees), per-run metadata, and task outputs.
• Aggregated tables (CSV) and paper-ready figures (PDF) for RQ1–RQ3.
• Scripts to reproduce the aggregation and plots.

Repository layout

• Model-level run outputs (per-model folders):

  • GPT-5/
  • GPT-4o-mini/
  • DeepSeek-V3-1/
  • DeepSeek-R1/
  • Gemini-2.5-flash/
  • Gemini-2.5-flash-nothinking/
  • Qwen3-235b/

• RQ-level analysis artifacts:

  • RQ1/: trajectory/success/retry evaluations and radar charts
  • RQ2/: agent-time vs. LLM/tool share breakdowns + plots
  • RQ3/: performance summaries (time/tokens), comparisons, and violin plots

• Trace processing utilities:

  • extract_trace_tree.py: generates execution_path.md from langfuse_trace.json
  • batch_extract_trees.sh: batch-generate execution_path.md for all runs
  • README-extract_trace_tree.md: detailed usage and output format

Model folders (per-run outputs)

Each model folder contains multiple application/architecture subfolders (e.g., BookWriter-MCP, SQLAssistant-A2A).

Architecture naming conventions

• No suffix: pure framework baseline (e.g., EmailResponder/, GameBuilder/, MarkdownValidator/)
• *-A2A: A2A protocol variant
• *-H_A2A: hard-coded / heterogeneous A2A baseline (in some plots/scripts, this may be treated as an A2A-mix / H-A2A variant)
• *-MCP: tool calling via MCP

Typical per-run structure

Under each application folder, test_results/ stores multiple runs (commonly run_*/ or session_*/ directories). A typical run contains:

• langfuse_trace.json: raw distributed tracing data for the run
• execution_path.md: extracted execution-path tree (generated by extract_trace_tree.py)
• metadata.json: run metadata
• Task outputs: generated artifacts such as books/chapters, reports, validation results, logs, etc.

Example:

RESULTS/
├── GPT-5/
│   └── BookWriter-MCP/
│       └── test_results/
│           └── run_YYYYMMDD_HHMMSS/
│               ├── langfuse_trace.json
│               ├── execution_path.md
│               ├── metadata.json
│               └── ... (task outputs)

RQ1: trajectory/success/retry evaluation

Location: RQ1/

• What it contains:

  • per-task evaluation scripts and reference trajectories (YAML)
  • aggregated CSV outputs (e.g., trajectory metrics, success rates, retry summaries)
  • radar charts under RQ1/RadarCharts/

• Entry points:

  • per-task scripts (run from a specific RQ1/<Task-Variant>/ folder):
    • evaluate_trajectory.py
    • evaluate_success.py
    • analyze_retry_patterns.py
  • plot generator (run from RQ1/):
    • generate_radar_charts.py

See RQ1/README.md for details.

RQ2: agent-time / LLM-share breakdown

Location: RQ2/

• What it contains:

  • per-task aggregation outputs (CSV) and mapping files (agent_map.md, FRAMEWORK_map.yaml)
  • consolidated summary tables (e.g., llm_share_summary.csv)
  • paper-ready figures (PDF), including heatmaps and bar charts

• Typical reproduction flow (from RQ2/):

  • python aggregate_llm_share.py
  • python plot_llm_share_heatmap.py
  • python plot_model_overhead_bars_a2a.py
  • python plot_model_overhead_bars_mix.py
  • python plot_framework_overhead_ratio_mix_vs_a2a.py
  • python plot_agent_time_share_bars_unified_y_by_series.py
  • python plot_total_classified_ecdf.py

See RQ2/README.md for details.

RQ3: performance analysis (time/tokens)

Location: RQ3/

• What it contains:

  • parsers/aggregators that scan execution_path.md across all runs
  • performance reports (CSV) under RQ3/performance_reports/
  • comparison reports under RQ3/agent_time_reports/
  • violin plots (PDF) under RQ3/Violin/

• Typical commands (from repository root):

  • python RQ3/analyze_performance.py
  • python RQ3/analyze_agent_time_comparisons.py
  • python RQ3/generate_summary.py
  • python RQ3/plot_total_tokens_violin.py

See RQ3/README.md for details.

Trace extraction: generating execution_path.md

execution_path.md is a key intermediate artifact consumed by RQ analyses.

• Single file:

python3 extract_trace_tree.py <path/to/langfuse_trace.json>

• Batch (scan the entire RESULTS/ tree and generate execution_path.md next to each trace):

./batch_extract_trees.sh

See README-extract_trace_tree.md for the output format and filtering rules.

Requirements

• Trace extraction (extract_trace_tree.py):

  • Python 3
  • standard library only

• RQ analyses and plotting (RQ1/, RQ2/, RQ3/):

  • Python 3
  • common scientific stack (e.g., numpy, pandas, matplotlib)

Notes

• Some scripts assume the default directory layout under RESULTS/. If you rename/move folders, update the corresponding path configuration inside the scripts.
• Trace files (langfuse_trace.json) can be large; many PDFs/CSVs in RQ1/, RQ2/, and RQ3/ are generated analysis artifacts.