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JetBrains/git_good_bench-lite
The declared licence (apache-2.0) is classified as permissive.
- Declared licence
- apache-2.0, from the tagEvidence: README.md at 086d113
- Licence files
- No licence file at the top level of the repositoryEvidence: file list at 086d113
- Declared sources
- None listed in the card metadata
- Access gated on Hugging Face
- No
Citation text from the card
@inproceedings{lindenbauer-etal-2025-gitgoodbench,
title = "{G}it{G}ood{B}ench: A Novel Benchmark For Evaluating Agentic Performance On Git",
author = "Lindenbauer, Tobias and
Bogomolov, Egor and
Zharov, Yaroslav",
editor = "Kamalloo, Ehsan and
Gontier, Nicolas and
Lu, Xing Han and
Dziri, Nouha and
Murty, Shikhar and
Lacoste, Alexandre",
booktitle = "Proceedings of the 1st Workshop for Research on Agent Language Models (REALM 2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.realm-1.19/",
doi = "10.18653/v1/2025.realm-1.19",
pages = "272--288",
ISBN = "979-8-89176-264-0",
abstract = "Benchmarks for Software Engineering (SE) AI agents, most notably SWE-bench, have catalyzed progress in programming capabilities of AI agents. However, they overlook critical developer workflows such as Version Control System (VCS) operations. To address this issue, we present GitGoodBench, a novel benchmark for evaluating AI agent performance on Version Control System (VCS) tasks. GitGoodBench covers three core Git scenarios extracted from permissive open-source Python, Java, and Kotlin repositories. Our benchmark provides three datasets: a comprehensive evaluation suite (900 samples), a rapid prototyping version (120 samples), and a training corpus (17,469 samples). We establish baseline p
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dataset_licence({ "id": "JetBrains/git_good_bench-lite" })
signal: "permissive"
stated_licence: "apache-2.0"
sources: 0