JetBrains-Research/lca-codegen-small

LCA Project Level Code Completion

How to load the dataset

from datasets import load_dataset

ds = load_dataset('JetBrains-Research/lca-codegen-small', 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.

Small dataset is defined by number of characters in .py files from the repository snapshot. This number is less than 48K.

Dataset Stats

• Number of datapoints: 144
• Number of repositories: 46
• Number of commits: 63

Completion File

• Number of lines, median: 310.5
• Number of lines, min: 201
• Number of lines, max: 1916

Repository Snapshot

• .py files: median 4, from 0 to 52
• non .py files: median 19.5, from 1 to 1044
• .py lines: median 128
• non .py lines: median 1227

Line Counts:

• infile: 1430
• inproject: 95
• common: 500
• committed: 1426
• non-informative: 532
• random: 703
• total: 4686

Scores

HF Space