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README.md
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license: apache-2.0
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---
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license: apache-2.0
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tags:
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- git
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- software-engineering
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size_categories:
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- n<1K
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---
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# Dataset Summary
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GitGoodBench Lite is a subset of 120 samples for evaluating the performance of AI agents in resolving git tasks (see Supported Scenarios).
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The samples in the dataset are evenly split across the programming languages Python, Java and Kotlin and the sample types merge conflict resolution and file-commit gram.
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This dataset thus contains 20 samples per sample type and programming language.
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All data in this dataset are collected from open-source repositories on GitHub with permissive licenses
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that have >= 1000 stars, >= 5 branches, >= 10 contributors and are not a fork or archived. We collected the initial list of repositories using [SEART.](https://seart-ghs.si.usi.ch/)
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Evaluation is to be performed by exact-match (EM) of diffs for the merge conflict setting and by LLM-as-a-Judge for the file-commit gram setting. [For further details see our paper.]()
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# Supported Tasks
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GitGoodBench Lite contains two types of samples: 'merge' and 'file_commit_chain'. All data required to setup the preconditions for a scenario are located in the `scenario` field.
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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
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generating commits based on the staged, uncommitted changes.
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## Merge
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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
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merges with exactly two parents in our dataset (no octopus merges).
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A merge scenario looks as follows:
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```
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{
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'merge_commit_hash': 'baa37f65fdff5b780a50d5b5c6bf8bc3ade43815',
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'parents': ['d758810c59a9134f437d60f73a82036749688ccb', '5dcd493c67ff863c69c1214f0892a80e4951087e'],
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'number_of_files_with_merge_conflict': 2,
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'total_number_of_merge_conflicts': 2,
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'files_in_merge_conflict': ['cogs/gpt_3_commands_and_converser.py', 'models/openai_model.py']
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}
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```
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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.
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## File-Commit Chain
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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.
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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
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improve the local tree via rebasing. Thus samples of this `sample_type` cover two scenario types.
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A `file_commit_chain` scenario looks as follows:
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```
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{
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'file': 'composer/models/huggingface.py',
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'branch': 'origin/vincent-mlflow-logger-verbose',
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'times_seen_consecutively': 3,
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'purity': 0.68,
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'newest_commit': 'c24b29f19c4c131a3ea7098dd8b8a5edde344819',
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'oldest_commit': 'c1ff80900f46d4e36feb4b326689fe14fc41cbc6'
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}
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```
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`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.
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# Dataset Structure
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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.
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| **Field** | **Type** | **Description** | **Is Metadata?** |
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|--------------------------|------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------|
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| **id** | string | A unique identifier for the dataset entry: <name>-<sample_type>-<running_index> | No |
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| **name** | string | The repository name, in “owner/repository” format. | No |
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| **default_branch** | string | The primary or default branch for the repository. | No |
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| **license** | string | Repository license. | Yes |
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| **stargazers** | integer | The number of stars on GitHub. | Yes |
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| **created_at** | string | The repository creation date. | Yes |
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| **topics** | string | A semicolon-delimited list of topics or tags associated with the repository. | Yes |
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| **programming_language** | string | The programming language of the sample. Possible values: "java," "python," or "kotlin." | No |
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| **scenario** | string | A JSON string describing specific scenario data (e.g., merge-conflict details, parent commits). | No |
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| **sample_type** | string | The type of sample. Possible values: "merge" or "file_commit_chain." | No |
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| **project_size** | string | Estimated size based on lines of code. Possible values: "tiny," "small," "medium," "large," or "huge." | Yes |
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| **project_activity** | string | How recently the project was active. Possible values: "day," "week," or "month." | Yes |
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| **difficulty** | string | The complexity level of the scenario. Possible values: "easy," "medium," or "hard." | Yes |
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**Note**:
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- Fields marked as **Is Metadata? = Yes** provide contextual information (e.g., project stats, licensing) rather than forming the core logic of a scenario.
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- Fields marked **No** represent the primary data for the scenario. Use them to inform or categorize the scenario type and project details.
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# Dataset statistics
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We provide some statistics on the distribution of “difficulty” within the overall dataset and across different scenario types.
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## Overall Difficulty Distribution
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| Difficulty | Fraction |
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|------------|----------|
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| easy | 0.375000 |
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| medium | 0.333333 |
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| hard | 0.291667 |
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## Difficulty Distribution for "merge" Scenarios
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| Difficulty | Fraction |
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|------------|----------|
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| easy | 0.516667 |
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| hard | 0.266667 |
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| medium | 0.216667 |
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## Difficulty Distribution for "file_commit_chain" Scenarios
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| Difficulty | Fraction |
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|------------|----------|
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| medium | 0.450000 |
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| hard | 0.316667 |
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| easy | 0.233333 |
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**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.
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