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README.md

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----
-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
-  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.
+---
+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
+  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**, after training cutoff of current frontier models (Claude Opus 4.6, OpenAI GPT-5.4, Gemini 3.1 Pro).
+
+## 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']))}")
+```
+
+## License
+
+MIT License. The dataset contains references to publicly available open-source code under their respective licenses.