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:

```text
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**:

```bash
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):

```bash
./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**.