Datasets:

JetBrains-Research
/

lca-codegen-medium

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: 514928459
      num_examples: 224
  download_size: 225824560
  dataset_size: 514928459
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-medium', 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.

Medium dataset is defined by number of characters in .py files from the repository snapshot. This number is from 48K to 192K.

Dataset Stats

Number of datapoints: 224
Number of repositories: 80
Number of commits: 175

Completion File

Number of lines, median: 310
Number of lines, min: 200
Number of lines, max: 1648

Repository Snapshot

.py files: median 34, from 3 to 117
non .py files: median 64.5, from 3 to 3977
.py lines: median 3786
non .py lines: median 9735

Line Counts:

infile: 2224
inproject: 2236
common: 779
committed: 1495
non-informative: 858
random: 1084
total: 8676

Scores

HF Space