| --- |
| license: apache-2.0 |
| task_categories: |
| - question-answering |
| - text-generation |
| language: |
| - en |
| - code |
| tags: |
| - code |
| - call-graph |
| - reasoning |
| - benchmark |
| - software-engineering |
| - agentic |
| - python |
| - typescript |
| size_categories: |
| - n<1K |
| --- |
| |
| # GraphCode-Bench-500-v0 |
|
|
| **GraphCode-Bench** is a benchmark for evaluating LLMs on *call-graph reasoning* — given a function in a real-world repository, can a model identify which functions call it (upstream) or which functions it calls (downstream), across 1 and 2 hops? |
|
|
| Models are evaluated **agentically**: they receive read-only filesystem tools (`list_directory`, `read_file`, `search_in_file`) and up to 10 turns to explore the codebase before producing an answer. |
|
|
| ## Dataset summary |
|
|
| | Split | Records | Repos | Languages | |
| |-------|---------|-------|-----------| |
| | `train` (bench500) | 483 | 22 | Python, TypeScript | |
|
|
| **Stratification**: 5 repos × 2 question types (upstream/downstream) × 2 hop depths (1-hop/2-hop). |
|
|
| ## Task definition |
|
|
| Each record contains: |
|
|
| - **anchor**: a named function in a real open-source repository |
| - **question_type**: `upstream` (who calls this?) or `downstream` (what does this call?) |
| - **hop_depth**: `1` (direct callers/callees) or `2` (one level further) |
| - **gold**: the ground-truth set of function names at each hop level (extracted via LSP) |
|
|
| Models must enumerate the correct function names. Scoring uses **set F1** against the gold answer. |
|
|
| ## Record schema |
|
|
| ```json |
| { |
| "sample_id": "psf__requests__send__upstream__1hop_abc123", |
| "repo": "psf/requests", |
| "question_type": "upstream", |
| "hop_depth": 1, |
| "gold": { |
| "hop_1": ["mount", "request"], |
| "hop_1_files": ["requests/sessions.py"] |
| }, |
| "metadata": { |
| "anchor": "send", |
| "anchor_file": "requests/adapters.py", |
| "anchor_source": "def send(self, request, ...):", |
| "result_size": 4, |
| "created_at": "2026-03-20T16:58:18.104721+00:00", |
| "file_content": "..." |
| } |
| } |
| ``` |
|
|
| ## Repositories included |
|
|
| **Python** (250 samples): `psf/requests`, `pallets/flask`, `pallets/click`, `scrapy/scrapy`, `celery/celery`, `encode/httpx`, `pytest-dev/pytest`, `psf/black`, `PyCQA/flake8`, `rq/rq`, `paramiko/paramiko` |
|
|
| **TypeScript** (233 samples): `sindresorhus/got`, `colinhacks/zod`, `trpc/trpc`, `immerjs/immer`, `node-fetch/node-fetch` |
|
|
| ## Pipeline |
|
|
| Ground truth is extracted by: |
| 1. Running [basedpyright](https://github.com/DetachHead/basedpyright) / typescript-language-server over each repo via LSP |
| 2. Walking call edges from the anchor to the requested depth |
| 3. Applying 15 quality filters (no builtins, no generics, minimum result size, etc.) |
|
|
| See the companion paper for full pipeline details. |
|
|
| ## Evaluation results (v0) |
|
|
| | Model | F1 | EM | Pass@0.5 | Avg Turns | |
| |-------|----|----|----------|-----------| |
| | GPT-5.4-nano (API)† | 0.364 | 0.170 | 0.400 | 6.19 | |
| | Qwen3-Coder-30B-A3B | 0.351 | 0.126 | 0.369 | 7.29 | |
| | GPT-OSS-20B | 0.313 | 0.116 | 0.362 | 7.72 | |
| | Mistral-Small-24B | 0.199 | 0.066 | 0.211 | 5.05 | |
|
|
| † Closed model, shown for reference. Open-weight models evaluated via vLLM on HPC cluster. |
|
|
| **Key finding**: 2-hop questions are 3–4× harder than 1-hop (Qwen3: F1=0.546 at 1-hop vs 0.151 at 2-hop). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{graphcodebench2026, |
| title = {GraphCode-Bench: Evaluating LLMs on Agentic Call-Graph Reasoning}, |
| author = {Rossi, Vittorio}, |
| year = {2026}, |
| url = {https://huggingface.co/datasets/VittorioRossi/GraphCode-Bench-500-v0} |
| } |
| ``` |
|
|
| ## License |
|
|
| Apache 2.0. The source code snippets included in `anchor_source` and `file_content` fields are derived from their respective open-source repositories under their original licenses. |
|
|
