hath995/DafnyGithub-Dataset

Dafny GitHub Dataset

A corpus of publicly available Dafny (.dfy) source code crawled from GitHub, packaged as JSONL for model training and analysis. Dafny is a verification-aware programming language with built-in specification constructs (pre/postconditions, invariants, assert/assume) checked by an SMT solver.

• 106,404 unique-by-content .dfy files
• 966 source repositories (of 1,018 discovered; forks excluded)
• 753 MB of source (stored as ~44 MB of zstd Parquet), median 5 files per repo
• Crawled 2026-07-22

Dataset structure

One JSON object per line:

field	type	description
repo	string	owner/name of the source repository
path	string	file path within the repo
license	string	SPDX id of the repo's license, or null
stars	int	repo stargazer count at crawl time
default_branch	string	branch the file was taken from
commit_sha	string	commit / tree sha the file was read from
pushed_at	string	repo last-push timestamp (ISO 8601)
size_bytes	int	file size in bytes
sha256	string	SHA-256 of the file content (deduplication key)
content	string	full UTF-8 source of the .dfy file

from datasets import load_dataset
ds = load_dataset("hath995/DafnyGithub-Dataset", split="train")
print(ds[0]["repo"], ds[0]["path"])
print(ds[0]["content"])

How it was built

1. Discovery — union of two GitHub channels: repo search language:Dafny (747 repos) and code search extension:dfy sliced by file size to beat the 1,000-result cap (surfacing .dfy files in otherwise non-Dafny repos). Forks are excluded (GitHub search excludes them by default).
2. Extraction — each repo shallow-cloned with a blobless partial + sparse checkout of *.dfy only (so a 2 GB repo pulls just its Dafny files). Repos that fail to check out on Windows (invalid paths, sparse-checkout edge cases) were recovered via the GitHub trees + raw-blob API.
3. Deduplication — files are deduplicated by exact content (sha256); ~117k exact duplicates were dropped during the crawl. Near-duplicates are not collapsed.

Considerations for using the data

Licensing

All files are included regardless of license; the license field records each repo's SPDX id so you can filter per your needs. A null or NOASSERTION value does not grant redistribution or training rights — filter accordingly before releasing or training.

Files by license: null/NONE 47,205 · NOASSERTION 33,730 · MIT 20,552 · Apache-2.0 3,880 · GPL-3.0 725 · BSD-2-Clause 94 · AGPL-3.0 73 · GPL-2.0 61 · BSD-3-Clause 33 · CC0-1.0 22 · BSL-1.0 12 · Unlicense 11 · CC-BY-4.0 4 · ISC 2. (NOASSERTION = a LICENSE file GitHub could not map to an SPDX id.)

Synthetic / benchmark corpora dominate the tail

A handful of repos are auto-generated verification benchmarks or LLM / mutation study data. They account for a large share of files, and because each differs by only a token or two, exact-content dedup does not collapse them:

repo	files	nature
VeriFixer/DSpec2Test	26,875	generated spec→test benchmark
ValentinaWu1112/…LLM-APR	11,119	LLM program-repair attempts
VeriFixer/DafnyFaultLoc	7,878	fault-localization benchmark
Beneficial-AI-Foundation/vericoding-benchmark	6,169	vericoding benchmark
MutDafny/mutdafny-study-data	4,652	mutation-testing variants (partial: GitHub truncated the tree)

For human-authored Dafny, filter these out:

DROP = {
    "VeriFixer/DSpec2Test", "VeriFixer/DafnyFaultLoc",
    "ValentinaWu1112/Automated-Program-Repair-of-Arithmetic-Programs-in-Dafny-using-Large-Language-Models",
    "Beneficial-AI-Foundation/vericoding-benchmark", "MutDafny/mutdafny-study-data",
}
ds_human = ds.filter(lambda r: r["repo"] not in DROP)

Limitations

• Snapshot of default branches at crawl time; no history, branches, or PRs.
• mutdafny-study-data is partial (GitHub truncated its recursive tree).
• Files larger than 2 MB were skipped.
• Coverage reflects GitHub's code-search index, which does not include every repo exhaustively.

Provenance

Reproducible via the crawler scripts (discover_repos.py, fetch_files.py, retry_api.py). Content belongs to the original repository authors under their respective licenses.