| --- |
| 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: 5220255729 |
| num_examples: 296 |
| download_size: 1810961403 |
| dataset_size: 5220255729 |
| 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-huge', 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. |
|
|
| Huge dataset is defined by number of characters in `.py` files from the repository snapshot. This number larger then 768K. |
|
|
| ## Dataset Stats |
|
|
| * Number of datapoints: 296 |
| * Number of repositories: 75 |
| * Number of commits: 252 |
|
|
| ### Completion File |
| * Number of lines, median: 313.5 |
| * Number of lines, min: 200 |
| * Number of lines, max: 1877 |
|
|
| ### Repository Snapshot |
| * `.py` files: <u>median 261</u>, from 47 to 5227 |
| * non `.py` files: <u>median 262</u>, from 24 to 7687 |
| * `.py` lines: <u>median 49811</u> |
| * non `.py` lines: <u>median 60163</u> |
|
|
| ### Line Counts: |
| * infile: 2608 |
| * inproject: 2901 |
| * common: 692 |
| * committed: 1019 |
| * non-informative: 1164 |
| * random: 1426 |
| * **total**: 9810 |
|
|
| ## Scores |
| [HF Space](https://huggingface.co/spaces/JetBrains-Research/long-code-arena) |
|
|
