| --- |
| annotations_creators: |
| - machine-generated |
| - expert-generated |
| language: |
| - en |
| - code |
| license: mit |
| multilinguality: |
| - monolingual |
| size_categories: |
| - n<1K |
| source_datasets: |
| - original |
| tags: |
| - code-search |
| - repository-retrieval |
| - context-budgeting |
| - agentic-coding |
| - evaluation-benchmark |
| task_categories: |
| - text-retrieval |
| - feature-extraction |
| task_ids: |
| - document-retrieval |
| pretty_name: RepoNeedle-Bench |
| dataset_info: |
| features: |
| - name: id |
| dtype: string |
| - name: repo_name |
| dtype: string |
| - name: task_type |
| dtype: string |
| - name: query |
| dtype: string |
| - name: gold_files |
| sequence: string |
| - name: anchor_files |
| sequence: string |
| - name: budget_tokens |
| dtype: int32 |
| - name: description |
| dtype: string |
| - name: is_held_out_repo |
| dtype: bool |
| splits: |
| - name: train |
| num_bytes: 8400 |
| num_examples: 10 |
| - name: test |
| num_bytes: 4200 |
| num_examples: 5 |
| download_size: 12600 |
| dataset_size: 12600 |
| --- |
| |
| # RepoNeedle-Bench: Repository Context Retrieval Benchmark for AI Coding Agents |
|
|
| > [!NOTE] |
| > **Corpus Scope**: This dataset (`RepoNeedle-Bench`) is a lightweight synthetic development and demonstration benchmark ($N=15$ samples: 10 train, 5 test across Flask, Requests, and FastAPI). It serves for fast local integration tests, intent classification validation, and budgeting verification. For large-scale external evaluation, RepoNeedle is independently evaluated on the complete official Agent Retrieval Bench (ARB v2: $N=427$ samples across 25 repositories and 271 frozen snapshots). |
|
|
| ## Dataset Description |
| **RepoNeedle-Bench** is a specialized, open-source benchmark designed to evaluate **upstream repository context retrieval** and **token-budget-aware context selection** for autonomous AI coding agents. |
|
|
| Rather than measuring end-to-end patch generation (where retrieval failures are masked by LLM synthesis capabilities), RepoNeedle-Bench isolates the critical upstream context acquisition phase across five distinct software engineering workflow categories: |
|
|
| 1. **`trace2code`**: Tracing execution error stack traces and exceptions down to root-cause implementation files. |
| 2. **`code2test`**: Retrieving regression and unit test suites implied by an implementation change or feature request. |
| 3. **`edit2ripple`**: Identifying downstream dependencies, callers, and interface implementers affected by an anchor file change. |
| 4. **`comment2context`**: Connecting high-level natural language issue comments and feature requests to core implementation files. |
| 5. **`selective`**: Recognizing out-of-repository, non-internal, or counterfactual queries where the retriever should abstain from returning hallucinated files. |
|
|
| ## Dataset Structure |
|
|
| Each sample in `train.jsonl` (development split) and `test.jsonl` (held-out split) follows the schema: |
|
|
| ```json |
| { |
| "id": "flask_trace2code_01", |
| "repo_name": "pallets/flask", |
| "task_type": "trace2code", |
| "query": "Traceback (most recent call last):\n File \"src/flask/app.py\", line 28, in wsgi_app...", |
| "gold_files": [ |
| "src/flask/wrappers.py", |
| "src/flask/app.py" |
| ], |
| "anchor_files": [], |
| "budget_tokens": 4000, |
| "description": "Traceback in get_json and dispatch_request.", |
| "is_held_out_repo": false |
| } |
| ``` |
|
|
| ## Data Provenance & Permissive Licensing |
| RepoNeedle-Bench is built exclusively from permissively licensed open-source repositories to ensure completely unrestricted downstream academic, personal, and commercial reuse: |
| - `pallets/flask` (BSD-3-Clause) |
| - `psf/requests` (Apache-2.0) |
| - `tiangolo/fastapi` (MIT License) |
|
|
| **No non-commercial or copyleft share-alike data (such as CC BY-NC-SA materials) is included.** |
|
|
| ## Benchmark Evaluation Metrics |
| Evaluations should measure both ranking quality and token budget efficiency: |
| - **Recall@k** (k=1, 3, 5) |
| - **Mean Reciprocal Rank (MRR)** |
| - **nDCG@k** |
| - **Budgeted Context Coverage (BCC@2k, BCC@4k, BCC@8k)**: The percentage of gold files present within a hard context token constraint. |
| - **Irrelevant Token Waste**: The number of non-gold tokens included in context. |
| - **Selective Abstention Accuracy**: Precision, recall, and F1 score on no-gold queries. |
|
|
| ## Citation |
| ```bibtex |
| @misc{reponeedle2026, |
| author = {RepoNeedle Contributors}, |
| title = {RepoNeedle-Bench: Repository Context Retrieval Benchmark for AI Coding Agents}, |
| year = {2026}, |
| publisher = {Hugging Face}, |
| howpublished = {\url{https://huggingface.co/datasets/RepoNeedle/RepoNeedle-Bench}} |
| } |
| ``` |
|
|
|
|
