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๐๏ธ Long Code Arena (Project-level code completion)
This is the benchmark for Project-level code completion task as part of the ๐๏ธ 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
โ repository name in the format{GitHub_user_name}__{repository_name}
commit_hash
โ commit hash of the repositorycompletion_file
โ dictionary with the completion file content in the following format:filename
โ path to the completion filecontent
โ content of the completion file
completion_lines
โ dictionary where the keys are categories of lines and values are a list of integers (numbers of lines to complete). The categories are:committed
โ line contains at least one function or class from the files that were added on the completion file commitinproject
โ line contains at least one function or class from the repository snapshot at the moment of completioninfile
โ line contains at least one function or class from the completion filecommon
โ line contains at least one function or class with common names, e.g.,main
,get
, etc.non_informative
โ line that was classified to be non-informative, e.g., too short, contains comments, etc.random
โ other lines.
repo_snapshot
โ dictionary with a snapshot of the repository before the commit. It has the same structure ascompletion_file
, but filenames and contents are orginized as lists.completion_lines_raw
โ same ascompletion_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
โ less than 48K characters;medium_context
โ from 48K to 192K characters;large_context
โ from 192K to 768K characters;huge_context
โ 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.
Citing
@article{bogomolov2024long,
title={Long Code Arena: a Set of Benchmarks for Long-Context Code Models},
author={Bogomolov, Egor and Eliseeva, Aleksandra and Galimzyanov, Timur and Glukhov, Evgeniy and Shapkin, Anton and Tigina, Maria and Golubev, Yaroslav and Kovrigin, Alexander and van Deursen, Arie and Izadi, Maliheh and Bryksin, Timofey},
journal={arXiv preprint arXiv:2406.11612},
year={2024}
}
You can find the paper here.
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