| --- |
| license: other |
| task_categories: [text-generation] |
| tags: [program-repair, code, swe-bench, sft, patchpilot] |
| --- |
| # PatchPilot patch-generation dataset |
|
|
| Supervised fine-tuning chats for PatchPilot's patch generator (A2). Each chat is exactly the |
| prompt PatchPilot's agent sends to its model, followed by the developers' real fix written in the |
| agent's SEARCH/REPLACE edit format. |
|
|
| ## Source |
|
|
| Built by `scripts/build_patchgen_data.py` (seed 42) from the **SWE-bench training split** |
| (`princeton-nlp/SWE-bench`, train) and the gold files' contents from `princeton-nlp/SWE-bench_oracle`. |
| The same seeded selection (at most 400 instances per repository) and the same repository-level |
| train/validation/test splits as the localization dataset are used. |
|
|
| ## Leakage check |
|
|
| The training split's 35 repositories are disjoint from SWE-bench Lite's 12. `scripts/check_leakage.py` |
| found zero overlap with all 300 SWE-bench Lite test instances on repository, instance id, |
| normalised issue text and normalised gold patch (`results/leakage_check.json`). |
|
|
| ## Construction |
|
|
| 1. **Filter (same shape as SWE-bench Lite's own selection):** the gold patch edits exactly one |
| existing non-test Python file, in at most three hunks. Larger multi-file fixes are skipped. |
| 2. **Target:** each hunk becomes a SEARCH/REPLACE block. If the original lines are not unique in |
| the file, real neighbouring lines are added until they are. Applying the blocks is checked to |
| give exactly the same file as `git apply` of the gold patch; examples that fail are skipped. |
| 3. **Prompt:** the agent's system prompt; a user turn with the issue text (max 3,000 characters), |
| the tests added by the fix (max 1,500 characters) and the gold file with line numbers, whole if |
| at most 150 lines, otherwise windows of 15 lines around each edit. |
| 4. **Length:** user + assistant at most 10,000 characters (about 2,800 Gemma tokens). |
|
|
| ## Size |
|
|
| From `results/patchgen/dataset_stats.json`: |
|
|
| | Split | Chats | |
| |---|---| |
| | train | 3,063 | |
| | val | 494 | |
| | test | 580 | |
|
|
| Of the 8,867 selected training-split instances, 4,137 became chats. Skipped: 4,113 not |
| SWE-bench-Lite-shaped (more than one file or more than three hunks), 235 too long, 197 with no |
| existing non-test Python file edited, 101 absent from `SWE-bench_oracle`, 78 whose diff could not |
| be turned into unique SEARCH/REPLACE blocks that reproduce `git apply`, and 6 whose edited file was |
| missing from the oracle text. User + assistant length: median 5,207 characters, maximum 9,995. |
| The splits use the same repositories as the localization dataset. |
|
|
| ## Format |
|
|
| `{train,val,test}.jsonl.gz`, one chat per line: |
|
|
| ```json |
| {"instance_id": "...", "repo": "owner/name", "split": "train", "files": ["pkg/mod.py"], |
| "messages": [{"role": "system", "content": "..."}, |
| {"role": "user", "content": "## Bug report ..."}, |
| {"role": "assistant", "content": "Fix:\n\npkg/mod.py\n<<<<<<< SEARCH\n..."}]} |
| ``` |
|
|
| ## Limitations |
|
|
| - The context shows the gold file around the edit, so the model learns to fix given good |
| localization; at run time it sees the agent's localized files, which may be wrong. |
| - The tests are shown as the test patch, not as the failing test output the agent sees at run time. |
| - Only small, single-file fixes; the developers' fix is one correct answer among possibly many. |
|
|
| ## Licence |
|
|
| Derived from SWE-bench (MIT). The underlying code belongs to the respective open-source projects |
| under their own licences. |
|
|
