# CrossCodeEval v2

## Dataset stats

- **6,710 v1 tasks → 863 retained; 5,847 newly archived.** All 863 retain their v1 repairs; v2 changes no task instructions, environments, verifiers or reference solutions.
- **7,763 original tasks: 863 retained and 6,900 archived**, including 1,053 earlier v1 exclusions.
- **Images:** the original 3 image definitions were not consolidated. Retained tasks reuse the 3 [v1 cached images](https://huggingface.co/datasets/FWeindel/validated-tasks/tree/main/crosscodeeval_v1/cached_images), pinned by immutable revision and checksum in the Parquet. No image copies are needed in v2.

## Dataset characteristics

Cross-file code completion in C#, Java, Python and TypeScript, with related-file context and executable verifiers.

<details><summary>Domains</summary>

Software Engineering; Code Completion; Cross-file Repository Understanding.

</details>

<details><summary>Capabilities</summary>

Read related files, resolve identifiers, follow existing code patterns and complete missing statements.

</details>

**Likely transfer targets:** cross-file code completion and repository understanding.

## Pass@16 filtering

Keep a task if either model solved it at least once. Both models completed 16 attempts on every v1 task. Recorded terminal failures count as unsuccessful attempts; unsolved tasks are not necessarily defective.

| Model | Tasks evaluated | Attempts | Tasks solved at least once |
| --- | ---: | ---: | ---: |
| Qwen3-30B Instruct | 6,710 | 107,360 | 641 |
| Qwen3-30B Thinking | 6,710 | 107,360 | 627 |
| Either model | 6,710 | 214,720 | **863** |

Successful-attempt distribution **among the 863 retained tasks**. Each model column totals 863; rows with zero tasks for both models are omitted. A task with 0/16 for one model was solved by the other.

| Successful attempts | Instruct tasks | Thinking tasks |
| --- | ---: | ---: |
| 0/16 | 222 | 236 |
| 1/16 | 222 | 193 |
| 2/16 | 70 | 94 |
| 3/16 | 57 | 64 |
| 4/16 | 40 | 51 |
| 5/16 | 38 | 36 |
| 6/16 | 33 | 22 |
| 7/16 | 33 | 24 |
| 8/16 | 20 | 24 |
| 9/16 | 22 | 21 |
| 10/16 | 17 | 16 |
| 11/16 | 21 | 16 |
| 12/16 | 18 | 19 |
| 13/16 | 17 | 12 |
| 14/16 | 13 | 13 |
| 15/16 | 7 | 7 |
| 16/16 | 13 | 15 |

The retained tasks inherit v1's rule-based repairs, build checks, reference reward 1 and no-op reward 0. Teacher runs used the same task contents without the timeout/anti-cheating sentence; v2 preserves that v1 sentence. [V1 repair details](https://huggingface.co/datasets/FWeindel/validated-tasks/blob/main/crosscodeeval_v1/README.md) · [Teacher results](https://huggingface.co/datasets/FWeindel/validated-tasks/discussions/6).

## Select tasks by successful-attempt count

`tasks.parquet` includes `instruct_successes`, `thinking_successes` and `attempts_per_model` (16). Counts are also available in [selection.json](assets/selection.json). Excluded payloads remain in the pinned v1 source recorded in `selection.json`; no duplicate archive Parquet is uploaded.

```python
import pyarrow.parquet as pq

# Exactly 1 successful attempt out of 16 for Instruct:
filters = [("instruct_successes", "=", 1)]

# Alternatively, at least 3 successes for either model:
# filters = [[("instruct_successes", ">=", 3)],
#            [("thinking_successes", ">=", 3)]]

subset = pq.read_table("tasks.parquet", filters=filters)
pq.write_table(subset, "selected.parquet")
```

`patch.py` reproduces the release from the pinned v1 Parquets and recorded counts.

## Runtime

- **Versions:** Apptainer **1.5.4**, Python **3.12.14**, Harbor **0.8.1**.
- **Implementation:** [Marin Harbor `7faf878c14b6`](https://github.com/marin-community/harbor/tree/7faf878c14b6d72737579ec936ebf5f74ba3194d) + [our runtime patches](https://github.com/MLI-lab/OT-next-data/tree/main/harbor_patches).

## Traces

[Trace Parquets](traces) contain **214,720 attempts**, including successful and failed attempts from every evaluated task, retained or excluded. Zstandard-compressed, one row per attempt; **1** rows have no saved conversation and are marked `trajectory_found=false`.

Columns include `task_id`, `model`, `attempt_id`, `reward`, `verifier_reward`, `retained_v2`, `trajectory_found`, and the full `trajectory` and `result` JSON. `reward` follows the published evaluation policy; `verifier_reward` preserves the raw verifier score. The manifest pins the original trace source.

```python
import json
import pyarrow.dataset as ds
traces = ds.dataset("traces", format="parquet", exclude_invalid_files=True)
failed = traces.to_table(filter=(ds.field("reward") == 0) & ds.field("trajectory_found"))
conversation = json.loads(failed["trajectory"][0].as_py())
```