# Claim 1: The SWE-fficiency benchmark comprises 498 real-world code optimization tasks drawn from 9 widely used Python repositories including numpy, pandas, and scipy (abstract only).


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## Verification

**Verdict: ✅ VERIFIED (full)**

The HuggingFace dataset `swefficiency/swefficiency` was inspected via the HuggingFace Datasets API and confirmed to contain exactly **498 instances** across **9 Python repositories**:

| Repository | Domain | Count |
|---|---|---|
| pandas-dev/pandas | Data manipulation (Python) | 279 |
| scipy/scipy | Scientific computing | 45 |
| sympy/sympy | Symbolic math | 39 |
| astropy/astropy | Astronomy | 35 |
| scikit-learn/scikit-learn | ML | 27 |
| numpy/numpy | Numerical computing | 23 |
| matplotlib/matplotlib | Plotting | 20 |
| dask/dask | Parallel computing | 19 |
| xarray/xarray | Labeled arrays | 11 |
| **Total** | | **498** |

### Dataset Features

Each instance includes:
- `repo`, `instance_id`, `base_commit` — source identification
- `patch` — expert's gold optimization patch (git diff format)
- `workload` — performance workload script (Python code)
- `speedup` — expert speedup factor (median=2.43x, up to 134x)
- `covering_tests` — repo unit tests exercising the edited code
- `PASS_TO_PASS` — test IDs that must stay passing

### Source

Dataset: [swefficiency/swefficiency](https://huggingface.co/datasets/swefficiency/swefficiency) on HuggingFace