|
--- |
|
dataset_info: |
|
features: |
|
- name: repo |
|
dtype: string |
|
- name: commit_hash |
|
dtype: string |
|
- name: completion_file |
|
struct: |
|
- name: filename |
|
dtype: string |
|
- name: content |
|
dtype: string |
|
- name: completion_lines |
|
struct: |
|
- name: infile |
|
sequence: int32 |
|
- name: inproject |
|
sequence: int32 |
|
- name: common |
|
sequence: int32 |
|
- name: commited |
|
sequence: int32 |
|
- name: non_informative |
|
sequence: int32 |
|
- name: random |
|
sequence: int32 |
|
- name: repo_snapshot |
|
sequence: |
|
- name: filename |
|
dtype: string |
|
- name: content |
|
dtype: string |
|
- name: completion_lines_raw |
|
struct: |
|
- name: commited |
|
sequence: int64 |
|
- name: common |
|
sequence: int64 |
|
- name: infile |
|
sequence: int64 |
|
- name: inproject |
|
sequence: int64 |
|
- name: non_informative |
|
sequence: int64 |
|
- name: other |
|
sequence: int64 |
|
splits: |
|
- name: test |
|
num_bytes: 2972013125 |
|
num_examples: 270 |
|
download_size: 1242136049 |
|
dataset_size: 2972013125 |
|
configs: |
|
- config_name: default |
|
data_files: |
|
- split: test |
|
path: data/test-* |
|
--- |
|
# LCA Project Level Code Completion |
|
## How to load the dataset |
|
``` |
|
from datasets import load_dataset |
|
|
|
ds = load_dataset('JetBrains-Research/lca-codegen-large', split='test') |
|
``` |
|
## Data Point Structure |
|
|
|
* `repo` – repository name in format `{GitHub_user_name}__{repository_name}` |
|
* `commit_hash` – commit hash |
|
* `completion_file` – dictionary with the completion file content in the following format: |
|
* `filename` – filepath to the completion file |
|
* `content` – content of the completion file |
|
* `completion_lines` – dictionary where keys are classes of lines and values are a list of integers (numbers of lines to complete). The classes are: |
|
* `committed` – line contains at least one function or class that was declared in the committed files from `commit_hash` |
|
* `inproject` – line contains at least one function or class that was declared in the project (excluding previous) |
|
* `infile` – line contains at least one function or class that was declared in the completion file (excluding previous) |
|
* `common` – line contains at least one function or class that was classified to be common, e.g., `main`, `get`, etc (excluding previous) |
|
* `non_informative` – line that was classified to be non-informative, e.g. too short, contains comments, etc |
|
* `random` – randomly sampled from the rest of the lines |
|
* `repo_snapshot` – dictionary with a snapshot of the repository before the commit. Has the same structure as `completion_file`, but filenames and contents are orginized as lists. |
|
* `completion_lines_raw` – the same as `completion_lines`, but before sampling. |
|
|
|
## How we collected the data |
|
|
|
To collect the data, we cloned repositories from GitHub where the main language is Python. |
|
The completion file for each data point is a `.py` file that was added to the repository in a commit. |
|
The state of the repository before this commit is the repo snapshot. |
|
|
|
Large dataset is defined by number of characters in `.py` files from the repository snapshot. This number is from 192K to 768K. |
|
|
|
## Dataset Stats |
|
|
|
* Number of datapoints: 270 |
|
* Number of repositories: 75 |
|
* Number of commits: 219 |
|
|
|
### Completion File |
|
* Number of lines, median: 278 |
|
* Number of lines, min: 200 |
|
* Number of lines, max: 1694 |
|
|
|
### Repository Snapshot |
|
* `.py` files: <u>median 84</u>, from 3 to 255 |
|
* non `.py` files: <u>median 155</u>, from 8 to 2174 |
|
* `.py` lines: <u>median 15466.5</u> |
|
* non `.py` lines: <u>median 18759</u> |
|
|
|
### Line Counts: |
|
* infile: 2691 |
|
* inproject: 2595 |
|
* common: 693 |
|
* committed: 1322 |
|
* non-informative: 1019 |
|
* random: 1311 |
|
* **total**: 9631 |
|
|
|
## Scores |
|
[HF Space](https://huggingface.co/spaces/JetBrains-Research/long-code-arena) |
