Datasets:

RepoNeedle
/

RepoNeedle-Bench

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

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:

trace2code: Tracing execution error stack traces and exceptions down to root-cause implementation files.
code2test: Retrieving regression and unit test suites implied by an implementation change or feature request.
edit2ripple: Identifying downstream dependencies, callers, and interface implementers affected by an anchor file change.
comment2context: Connecting high-level natural language issue comments and feature requests to core implementation files.
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:

{
  "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

@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}}
}