metadata

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: median 84, from 3 to 255
non .py files: median 155, from 8 to 2174
.py lines: median 15466.5
non .py lines: median 18759

Line Counts:

infile: 2691
inproject: 2595
common: 693
committed: 1322
non-informative: 1019
random: 1311
total: 9631

Scores

HF Space