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---
configs:
- config_name: python
data_files:
- split: test
path: data/python/*.json
---
# 🏟️ Long Code Arena (CI Fixing)
> 🛠️ CI Fixing: given logs of a failed GitHub Actions workflow and the corresponding repository shapshot, fix the
> repository contents in order to make the workflow pass.
This is the benchmark for **CI Fixing** task as part of
🏟️ [**Long Code Arena** benchmark](https://huggingface.co/spaces/JetBrains-Research/long-code-arena).
To score your model on this dataset, you can use **CI Fixing benchmark**
(https://github.com/JetBrains-Research/lca-baselines/tree/main/ci-fixing/ci-fixing-benchmark)
## How-to
1. List all the available configs
via [`datasets.get_dataset_config_names`](https://huggingface.co/docs/datasets/v2.14.3/en/package_reference/loading_methods#datasets.get_dataset_config_names)
and choose an appropriate one.
Current configs: `python`
2. Load the data
via [`load_dataset`](https://huggingface.co/docs/datasets/v2.14.3/en/package_reference/loading_methods#datasets.load_dataset):
```
from datasets import load_dataset
dataset = load_dataset("JetBrains-Research/lca-ci-fixing", split="test")
```
Note that all the data we have is considered to be in the test split.
**NOTE**: If you encounter any errors with loading the dataset on Windows, update the datasets library (was tested on datasets==2.16.1)
## Dataset Structure
This dataset contains logs of the failed GitHub Action workflows for some commits
followed by the commit that passes the workflow successfully.
Note that, unlike many other 🏟 Long Code Arena datasets, this dataset doesn't contain repositories.
* Our [CI Fixing benchmark](https://github.com/JetBrains-Research/lca-baselines/tree/main/ci-fixing/ci-fixing-benchmark) clones the necessary repos to the user's local machine. The user should run
their model to
fix the failing CI workflows, and the benchmark will push commits to GitHub, returning the results of the workflow
runs
for all the datapoints.
### Datapoint Schema
Each example has the following fields:
| Field | Description |
|---------------------|------------------------------------------------------------------------------------------------------------------------------|
| `contributor` | Username of the contributor that committed changes |
| `difficulty` | Difficulty of the problem (assessor-based. 0 means that fix requires only the code formatting) |
| `diff` | Contents of the diff between the failed and the successful commits |
| `head_branch` | Name of the original branch that the commit was pushed at |
| `id` | Unique ID of the datapoint |
| `language` | Main language of the repo |
| `logs` | List of dicts with keys `log` (logs of the failed job, particular step) and `step_name` (name of the failed step of the job) |
| `repo_name` | Name of the original repo (second part of the `owner/name` on GitHub) |
| `repo owner` | Owner of the original repo (first part of the `owner/name` on GitHub) |
| `sha_fail` | SHA of the failed commit |
| `sha_success` | SHA of the successful commit |
| `workflow` | Contents of the workflow file |
| `workflow_filename` | The name of the workflow file (without directories) |
| `workflow_name` | The name of the workflow |
| `workflow_path` | The full path to the workflow file
| `changed_files` | List of files changed in diff
| `commit_link` | URL to commit corresponding to failed job
### Datapoint Example
```
{'contributor': 'Gallaecio',
'diff': 'diff --git a/scrapy/crawler.py b/scrapy/crawler.py/n<...>',
'difficulty': '1',
'head_branch': 'component-getters',
'id': 18,
'language': 'Python',
'logs': [{'log': '##[group]Run pip install -U tox\n<...>',
'step_name': 'checks (3.12, pylint)/4_Run check.txt'}],
'repo_name': 'scrapy',
'repo_owner': 'scrapy',
'sha_fail': '0f71221cf9875ed8ef3400e1008408e79b6691e6',
'sha_success': 'c1ba9ccdf916b89d875628ba143dc5c9f6977430',
'workflow': 'name: Checks\non: [push, pull_request]\n\n<...>',
'workflow_filename': 'checks.yml',
'workflow_name': 'Checks',
'workflow_path': '.github/workflows/checks.yml',
'changed_files': ["scrapy/crawler.py"],
'commit_link': "https://github.com/scrapy/scrapy/tree/0f71221cf9875ed8ef3400e1008408e79b6691e6"}
``` |