gitgood/git_good_bench-train

Dataset Summary

GitGoodBench Lite is a subset of 17469 samples for collecting trajectories of AI agents resolving git tasks (see Supported Scenarios) for model training purposes. We support the programming languages Python, Java and Kotlin and the sample types merge conflict resolution and file-commit chain.

All data in this dataset are collected from 816 unique, open-source GitHub repositories with permissive licenses that have >= 1000 stars, >= 5 branches, >= 10 contributors and are not a fork or archived. We collected the initial list of repositories using SEART.

For further details see our paper.

Supported Tasks

GitGoodBench Lite contains two types of samples: 'merge' and 'file_commit_chain'. It is important to note that the sample type 'file_commit_chain' can be used for two scenario types: Performing an interactive rebase to clean up the local tree or iteratively generating commits based on the staged, uncommitted changes.

Merge

Merge scenarios are contain one or more merge conflicts that occurred during a merge. All merge conflicts are guaranteed to be in a Python, Java or Kotlin file. There are only merges with exactly two parents in our dataset (no octopus merges).

A merge scenario looks as follows:

{
  'merge_commit_hash': '9bcf252fb11ec692dfbc152933dddd427098dcc9',
  'parents': ['5d5df76aa7df56bdbec07c18e063a1125cfd0465', '3bf663778b2a56c614818069043354d4b6d5f156'],
  'number_of_files_with_merge_conflict': 1,
  'total_number_of_merge_conflicts': 2,
  'files_in_merge_conflict': ['models/index_model.py']
}

Where merge_commit_hash contains the ground truth merge commit and the parents are the commits during the merge of which the conflict(s) in files_in_merge_conflict occurred.

File-Commit Chain

File-commit chain scenarios consist of two commits, the oldest and newest commit. In all commits between the oldest_commit and newest_commit (inclusive) file was modified. In total the chain consists of times_seen_consecutively commits. The intended use-cases of these scenarios are to evaluate the agent's capacity to create meaningful, cohesive commits or improve the local tree via rebasing. Thus samples of this sample_type cover two scenario types.

File-commit chains are at least 3 commits long, the file the sample concerns itself with is guaranteed to be of programming_language (this is not the case for other potential files in the commits of the sample) and no commit is a merge commit.

A file_commit_chain scenario looks as follows:

{
  'file': 'torchaudio/transforms/_transforms.py',
  'branch': 'main',
  'times_seen_consecutively': 3,
  'purity': 0.69,
  'newest_commit': '7ac3e2e237e443baf91dfbf9893fca114c1c6001',
  'oldest_commit': '3742cebb7dc0f8adf24f4ee1cea368195c448f78'
}

purity indicates the relative amount of changes in the chain that occurred solely in file and is a heuristic for the difficulty of the scenario. We expect noisier scenarios to be more difficult.

Dataset Structure

The following table provides per-field details. Columns marked “Yes” under Is Metadata? are those that provide contextual or descriptive information but are not essential to the primary scenario logic.

Field	Type	Description	Is Metadata?
id	string	A unique identifier for the dataset entry: --	No
name	string	The repository name, in “owner/repository” format.	No
default_branch	string	The primary or default branch for the repository.	No
license	string	Repository license.	Yes
stargazers	integer	The number of stars on GitHub.	Yes
created_at	string	The repository creation date.	Yes
topics	string	A semicolon-delimited list of topics or tags associated with the repository.	Yes
programming_language	string	The programming language of the sample. Possible values: "java," "python," or "kotlin."	No
scenario	string	A JSON string describing specific scenario data (e.g., merge-conflict details, parent commits).	No
sample_type	string	The type of sample. Possible values: "merge" or "file_commit_chain."	No
project_size	string	Estimated size based on lines of code. Possible values: "tiny," "small," "medium," "large," or "huge."	Yes
difficulty	string	The complexity level of the scenario. Possible values: "easy," "medium," or "hard."	Yes

Note:

• Fields marked as Is Metadata? = Yes provide contextual information (e.g., project stats, licensing) rather than forming the core logic of a scenario.
• Fields marked No represent the primary data for the scenario. Use them to inform or categorize the scenario type and project details.

Dataset statistics

We provide some statistics on the diversity of our dataset with respect to repositories, programming languages and merge conflict resolution samples.

Dataset Skew

The below statistics show that our dataset does not exhibit an extreme skew towards some repositories and is relatively well balanced with respect to source repositories.

Distribution Statistics

• Total number of repositories analyzed: 816
• Average (mean) samples per repository: 21.4
• Standard deviation (std): 48.8
• Minimum (min): 1
• 25th percentile (25%): 2
• Median (50%): 6
• 75th percentile (75%): 18
• Maximum (max): 644

Top-10 Repositories by Sample Count

Repository	Percentage of Total Samples
zulip/zulip	3.69%
trinodb/trino	2.47%
wandb/wandb	2.46%
facebook/litho	2.16%
oss-review-toolkit/ort	1.96%
apache/tomcat	1.94%
nvidia/nemo	1.76%
h2oai/h2ogpt	1.32%
conan-io/conan	1.30%
huggingface/transformers	1.05%

Distribution of Programming Languages

We do however note a severe skew towards Python and Java with only 3.8% of samples being Kotlin.

Programming Language	Count	Percentage
python	10985	62.82%
java	5881	33.67%
kotlin	603	3.45%

Difficulty Distribution for "merge" Scenarios

Difficulty	Proportion
easy	0.516466
hard	0.299672
medium	0.183861

Languages We note that the text data in this dataset consists mostly of: commit messages, comments and is primarily in English. We do however not filter for any human languages explcitly.