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 hashcompletion_file
– dictionary with the completion file content in the following format:filename
– filepath to the completion filecontent
– content of the completion filecompletion_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 fromcommit_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, etcrandom
– randomly sampled from the rest of the linesrepo_snapshot
– dictionary with a snapshot of the repository before the commit. Has the same structure ascompletion_file
, but filenames and contents are orginized as lists.completion_lines_raw
– the 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 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