README.md

---

dataset_info:
  features:
    - name: instance_id
      dtype: string
    - name: repo
      dtype: string
    - name: base_commit
      dtype: string
    - name: problem_statement
      dtype: string
    - name: test_patch
      dtype: string
    - name: human_patch
      dtype: string
    - name: pr_number
      dtype: int64
    - name: pr_url
      dtype: string
    - name: pr_merged_at
      dtype: string
    - name: issue_number
      dtype: int64
    - name: issue_url
      dtype: string
    - name: human_changed_lines
      dtype: int64
    - name: FAIL_TO_PASS
      dtype: string
    - name: PASS_TO_PASS
      dtype: string
    - name: version
      dtype: string
  splits:
    - name: test
      num_examples: 119
license: mit
task_categories:
  - other
language:
  - en
tags:
  - code-generation
  - software-engineering
  - complexity
  - swe-bench
  - contamination-free
  - post-training-cutoff
pretty_name: SWE-bench Complex
size_categories:
  - n<1K
---


# SWE-bench Complex

**A contamination-free, complexity-focused evaluation set for AI coding agents.**

SWE-bench Complex is a curated dataset of **119 real-world GitHub issues** from major Python open-source projects, designed specifically for studying code complexity in AI-generated patches. All tasks were merged between **January–March 2026**, guaranteeing they postdate the training cutoff of current frontier models.

## Why SWE-bench Complex?

Existing benchmarks like [SWE-bench Verified](https://huggingface.co/datasets/princeton-nlp/SWE-bench_Verified) suffer from two problems for complexity research:

### 1. Data Contamination

Over 94% of SWE-bench issues predate current LLM training cutoffs. Aleithan et al. found that **32.67% of successful patches involve "cheating"** through solution leakage, and resolution rates dropped from 12.47% to 3.97% when leaked instances were filtered out ([SWE-bench+, 2024](https://arxiv.org/abs/2410.06992)).

All SWE-bench Complex instances postdate the training cutoffs of:

| Model | Provider | Training Cutoff | Gap |
|---|---|---|---|
| Claude Opus 4.6 | Anthropic | Oct 2025 | 3+ months |
| GPT-5.3-Codex | OpenAI | Sep 2025 | 4+ months |
| GPT-5.4 | OpenAI | Nov 2025 | 2+ months |
| Gemini 3.1 Pro | Google | Oct 2025 | 3+ months |

### 2. Trivial Patches

SWE-bench Verified has a median patch size of just **7 changed lines** — 44.6% of tasks require only 1–5 lines. These trivial patches yield near-zero complexity deltas, reducing statistical power for quality studies.

SWE-bench Complex targets **substantive patches** with a median of **48 changed lines** — 6.9× larger than SWE-bench Verified.

## Dataset Comparison

| Characteristic | SWE-bench Verified | SWE-bench Complex |
|---|---|---|
| Tasks | 500 | 119 |
| Repositories | 12 | 8 |
| Median changed lines | 7 | **48** |
| Mean changed lines | 14.3 | **74.9** |
| Mean Python files changed | 1.2 | **3.9** |
| Human ΔCC (mean) | +1.14 | **+4.06** |
| Human ΔLLOC (mean) | +2.77 | **+19.08** |
| Human ΔMI (mean) | −0.230 | **−0.417** |
| Human ΔCogC (mean) | N/A | **+3.63** |
| Post-training-cutoff | <6% | **100%** |

Complexity metrics measured using [Wily v2](https://github.com/tonybaloney/wily):
- **ΔCC**: Cyclomatic Complexity change (McCabe, 1976)
- **ΔLLOC**: Logical Lines of Code change
- **ΔMI**: Maintainability Index change (Oman & Hagemeister, 1992)
- **ΔCogC**: Cognitive Complexity change (Campbell, 2018)

## Repository Distribution

| Repository | Instances |
|---|---|
| django/django | 38 |
| astropy/astropy | 22 |
| pydata/xarray | 17 |
| scikit-learn/scikit-learn | 14 |
| pylint-dev/pylint | 10 |
| matplotlib/matplotlib | 9 |
| sympy/sympy | 8 |
| pallets/flask | 1 |

## Selection Criteria

Instances were collected from merged pull requests in the SWE-bench ecosystem repositories with the following filters:

1. **Date range**: Merged January 1 – March 10, 2026 (post-training-cutoff)
2. **Issue linkage**: PR explicitly references a GitHub issue via "fixes #N" or equivalent
3. **Test coverage**: PR includes both implementation and test changes to Python files
4. **Minimum complexity**: Implementation patch modifies ≥4 changed lines
5. **Python files**: Only `.py` file changes retained
6. **Manual review**: Each candidate reviewed for solvability — documentation-only changes, large-scale refactors (>300 lines or >10 files), and tasks requiring external domain knowledge were excluded

From 1,043 scraped PRs → 712 with issue references → 224 after automated filters → **119 after manual review**.

## Schema

Each instance contains:

| Field | Type | Description |
|---|---|---|
| `instance_id` | string | Unique identifier (`{owner}__{repo}-{pr_number}`) |
| `repo` | string | GitHub repository (`owner/repo`) |
| `base_commit` | string | Parent commit SHA |
| `problem_statement` | string | GitHub issue text (title + body) |
| `test_patch` | string | Unified diff of test-file changes |
| `human_patch` | string | Unified diff of implementation-file changes |
| `pr_number` | int | Pull request number |
| `pr_url` | string | Pull request URL |
| `pr_merged_at` | string | Merge timestamp (ISO 8601) |
| `issue_number` | int | Referenced issue number |
| `issue_url` | string | Issue URL |
| `human_changed_lines` | int | Total changed lines in the human patch |
| `FAIL_TO_PASS` | string | JSON array of test IDs that must go FAIL→PASS |
| `PASS_TO_PASS` | string | JSON array of test IDs that must remain PASS |

## SWE-bench Compatibility

SWE-bench Complex uses the same schema as SWE-bench Verified and can be evaluated using the standard [SWE-bench harness](https://github.com/princeton-nlp/SWE-bench):

```bash

python -m swebench.harness.run_evaluation \

    -d anthonypjshaw/SWE-bench_Complex \

    -s test \

    -p predictions.jsonl \

    -id my_run \

    --max_workers 4

```

## Usage

```python

from datasets import load_dataset



dataset = load_dataset("anthonypjshaw/SWE-bench_Complex", split="test")

print(f"Tasks: {len(dataset)}")

print(f"Repos: {len(set(dataset['repo']))}")

```

## Citation

If you use SWE-bench Complex in your research, please cite:

```bibtex

@inproceedings{Shaw2026SWEbenchComplex,

  author    = {Shaw, Anthony},

  title     = {Beyond the Benchmark: A Contamination-Free Study of {AI} Code Complexity Across Four Frontier Models},

  booktitle = {Proceedings of the IEEE International Conference on Software Engineering (SSE)},

  year      = {2026},

}

```

## License

MIT License. The dataset contains references to publicly available open-source code under their respective licenses.