| --- |
| language: |
| - code |
| license: mit |
| task_categories: |
| - graph-ml |
| - text-classification |
| tags: |
| - code |
| - ast |
| - gnn |
| - graph-neural-network |
| - ruby |
| - complexity-prediction |
| - code-generation |
| - negative-results |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # GNN Ruby Code Study |
|
|
| Systematic study of Graph Neural Network architectures for Ruby code complexity prediction and generation. |
|
|
| **Paper:** [Graph Neural Networks for Ruby Code Complexity Prediction and Generation: A Systematic Architecture Study](paper.md) |
|
|
| ## Dataset |
|
|
| **22,452 Ruby methods** parsed into AST graphs with 74-dimensional node features. |
|
|
| | Split | Samples | File | |
| |-------|---------|------| |
| | Train | 19,084 | `dataset/train.jsonl` | |
| | Validation | 3,368 | `dataset/val.jsonl` | |
|
|
| Each JSONL record contains: |
| - `repo_name`: Source repository |
| - `file_path`: Original file path |
| - `raw_source`: Raw Ruby source code |
| - `complexity_score`: McCabe cyclomatic complexity |
| - `ast_json`: Full AST as nested JSON (node types + literal values) |
| - `id`: Unique identifier |
|
|
| ### Node Features (74D) |
| - One-hot encoding of 73 AST node types (def, send, args, lvar, str, ...) + 1 unknown |
| - Types cover Ruby AST nodes; literal values (identifiers, strings, numbers) map to unknown |
|
|
| ## Key Findings |
|
|
| 1. **5-layer GraphSAGE** achieves MAE 4.018 (R² = 0.709) for complexity prediction — 16% better than 3-layer baseline (9.9σ significant) |
| 2. **GNN autoencoders produce 0% valid Ruby** across all 15+ tested configurations |
| 3. **The literal value bottleneck**: Teacher-forced GIN achieves 81% node type accuracy and 99.5% type diversity, but 0% syntax validity because 47% of AST elements are literals with no learnable representation |
| 4. **Chain decoders collapse**: 93% of predictions default to UNKNOWN without structural supervision |
| 5. **Total cost: ~$4.32** across 51 GPU experiments on Vast.ai RTX 4090 + local RTX 2070 SUPER |
|
|
| ## Repository Structure |
|
|
| ``` |
| ├── paper.md # Full research paper |
| ├── dataset/ |
| │ ├── train.jsonl # 19,084 Ruby methods (37 MB) |
| │ └── val.jsonl # 3,368 Ruby methods (6.5 MB) |
| ├── models/ |
| │ ├── encoder_sage_5layer.pt # Pre-trained SAGE encoder |
| │ └── decoders/ # Trained decoder checkpoints |
| │ ├── tf-gin-256-deep.pt # Best: teacher-forced GIN, 5 layers |
| │ ├── tf-gin-{128,256,512}.pt # Dimension ablation |
| │ └── chain-gin-256.pt # Control (no structural supervision) |
| ├── results/ |
| │ ├── fleet_experiments.json # All Vast.ai experiment metrics |
| │ ├── autonomous_research.json # 18 baseline variance replicates |
| │ └── gin_deep_dive/ # Local deep-dive analysis |
| │ ├── summary.json # Ablation summary table |
| │ └── *_results.json # Per-config detailed results |
| ├── experiments/ # Ratiocinator fleet YAML specs |
| ├── specs/ # Ratiocinator research YAML specs |
| ├── src/ # Model source code |
| │ ├── models.py # GNN architectures |
| │ ├── data_processing.py # AST→graph pipeline |
| │ ├── loss.py # Loss functions |
| │ ├── train.py # Complexity prediction trainer |
| │ └── train_autoencoder.py # Autoencoder trainer |
| └── scripts/ # Runner and evaluation scripts |
| ``` |
|
|
| ## Reproducing Results |
|
|
| ```bash |
| # Clone the experiment branch |
| git clone -b experiment/ratiocinator-gnn-study https://github.com/timlawrenz/jubilant-palm-tree |
| cd jubilant-palm-tree |
| |
| # Install dependencies |
| python -m venv .venv && source .venv/bin/activate |
| pip install torch torchvision torch_geometric |
| |
| # Train complexity prediction (Track 1) |
| python train.py --conv_type SAGE --num_layers 5 --epochs 50 |
| |
| # Train autoencoder with teacher-forced GIN decoder (Track 4) |
| python train_autoencoder.py --decoder_conv_type GIN --decoder_edge_mode teacher_forced --epochs 30 |
| |
| # Run the full deep-dive ablation |
| python scripts/gin_deep_dive.py |
| ``` |
|
|
| ## Source Code |
|
|
| - **Model code:** [jubilant-palm-tree](https://github.com/timlawrenz/jubilant-palm-tree) (branch: `experiment/ratiocinator-gnn-study`) |
| - **Orchestrator:** [ratiocinator](https://github.com/timlawrenz/ratiocinator) |
|
|
| ## Citation |
|
|
| If you use this dataset or findings, please cite: |
| ``` |
| @misc{lawrenz2025gnnruby, |
| title={Graph Neural Networks for Ruby Code Complexity Prediction and Generation: A Systematic Architecture Study}, |
| author={Tim Lawrenz}, |
| year={2025}, |
| howpublished={\url{https://huggingface.co/datasets/timlawrenz/gnn-ruby-code-study}} |
| } |
| ``` |
|
|
