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metadata
license: apache-2.0
dataset_info:
  - config_name: default
    features:
      - name: repo
        dtype: string
      - name: commit_hash
        dtype: string
      - name: completion_file
        struct:
          - name: filename
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          - name: content
            dtype: string
      - name: completion_lines
        struct:
          - name: infile
            sequence: int32
          - name: inproject
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          - name: common
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          - name: commited
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          - name: non_informative
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          - name: random
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      - name: repo_snapshot
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          - name: content
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      - name: completion_lines_raw
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          - name: common
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          - name: infile
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          - name: inproject
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          - name: non_informative
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          - name: other
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      - name: commit_hash
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          - name: inproject
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          - name: non_informative
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          - name: other
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    splits:
      - name: test
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        num_examples: 296
    download_size: 1811094462
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  - config_name: large_context
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      - name: commit_hash
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      - name: completion_file
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      - name: completion_lines
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      - name: commit_hash
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      - name: repo
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      - name: commit_hash
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configs:
  - config_name: huge_context
    data_files:
      - split: test
        path: data/huge_context/test-*
  - config_name: large_context
    data_files:
      - split: test
        path: data/large_context/test-*
  - config_name: medium_context
    data_files:
      - split: test
        path: data/medium_context/test-*
  - config_name: small_context
    data_files:
      - split: test
        path: data/small_context/test-*

🏟️ Long Code Arena (Project-level code completion)

This is the benchmark for Project-level code completion task as part of the 🏟️ Long Code Arena benchmark. Each datapoint contains the file for completion, a list of lines to complete with their categories (see the categorization below), and a repository snapshot that can be used to build the context. All the repositories are published under permissive licenses (MIT, Apache-2.0, BSD-3-Clause, and BSD-2-Clause). The datapoints can be removed upon request.

How-to

Load the data via load_dataset:

from datasets import load_dataset

config_names = [
  'small_context',
  'medium_context',
  'large_context',
  'huge_context'
]

ds = load_dataset('JetBrains-Research/lca-project-level-code-completion', config_name, split='test')

Dataset Structure

Datapoints in the dataset have the following structure:

  • repo – repository name in the format {GitHub_user_name}__{repository_name}
  • commit_hash – commit hash of the repository
  • completion_file – dictionary with the completion file content in the following format:
    • filename – path to the completion file
    • content – content of the completion file
  • completion_lines – dictionary where the keys are categories of lines and values are a list of integers (numbers of lines to complete). The categories are:
    • committed – line contains at least one function or class from the files that were added on the completion file commit
    • inproject – line contains at least one function or class from the repository snapshot at the moment of completion
    • infile – line contains at least one function or class from the completion file
    • common – line contains at least one function or class with common names, e.g., main, get, etc.
    • non_informative – line that was classified to be non-informative, e.g., too short, contains comments, etc.
    • random – other lines.
  • repo_snapshot – dictionary with a snapshot of the repository before the commit. It has the same structure as completion_file, but filenames and contents are orginized as lists.
  • completion_lines_raw – 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 datapoint 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.

The dataset configurations are based on the number of characters in .py files from the repository snapshot:

  • small_context – less than 48K characters;
  • medium_context – from 48K to 192K characters;
  • large_context – from 192K to 768K characters;
  • huge_context – more than 768K characters.

Datasets Stats

Dataset Number of datapoints Number of repositories Number of commits
small_context 144 46 63
medium_context 224 80 175
large_context 270 75 219
huge_context 296 75 252

Completion File

Dataset Completion file lines, min Completion file lines, max Completion file lines, median
small_context 201 1916 310.5
medium_context 200 1648 310.0
large_context 200 1694 278.0
huge_context 200 1877 313.5

Repository Snapshot .py files

Dataset Context py files number, min Context py files number, max Context py files number, median Context py lines, median
small_context 0 52 4.0 128.0
medium_context 3 117 34.0 3786.0
large_context 3 255 84.0 15466.5
huge_context 47 5227 261.0 49811.0

Repository Snapshot non .py files

Dataset Context non-py files number, min Context non-py files number, max Context non-py files number, median Context non-py lines, median
small_context 1 1044 19.5 1227.0
medium_context 3 3977 64.5 9735.0
large_context 8 2174 155.0 18759.0
huge_context 24 7687 262.0 60163.0

Line Counts:

Dataset infile inproject common commited non-informative random all
small_context 1430 95 500 1426 532 703 4686
medium_context 2224 2236 779 1495 858 1084 8676
large_context 2691 2595 693 1322 1019 1311 9631
huge_context 2608 2901 692 1019 1164 1426 9810

Scores

You can find the results of running various models on this dataset in our leaderboard.