Datasets:

thanatchon36
/

coauthored

license: cc-by-4.0
pretty_name: 'coauthored: AI-attributed commits in AI infrastructure repositories, 2023–2026'
language:
  - en
size_categories:
  - 10K<n<100K
tags:
  - software-engineering
  - mining-software-repositories
  - git
  - ai-assisted-programming
  - provenance
configs:
  - config_name: repo_month
    data_files: aggregated/repo_month.parquet
  - config_name: repo_month_tool
    data_files: aggregated/repo_month_tool.parquet

coauthored: AI-attributed commits in AI infrastructure repositories, 2023–2026

Monthly counts of AI-attributed commits from the default-branch history of 96 public GitHub repositories, cloned on one UTC day (2026-10-28): 42 repositories that train, fine-tune, evaluate and serve AI models (S1), 12 open-source coding-agent harnesses (S2), and 42 popular non-AI repositories matched on stars and activity (S3). Non-merge commits in the study window (2023-01 to 2026-09, primary specification, before shared-history deduplication): S1 407,527, S2 118,672, S3 374,109.

A commit is AI-attributed when its message carries a co-author trailer naming an AI tool, its author or committer is an AI-agent account, or it carries a tool's generated-with marker, as listed in the pre-registered signature catalogue (configs/signatures.yaml, three public evidence URLs per entry). Attribution is a lower bound on AI involvement: tools can be configured not to attribute, attribution can be lost at merge, and some tools never attribute. An attributed commit is not a commit "written by AI".

Paper: How Much of the Code That Trains the Models Declares AI Help? (PDF). Code: https://github.com/thanatchon36/coauthored.

Independent personal research. Public data only. Does not represent the views of any employer or institution.

Privacy

As pre-registered (§11), only aggregates are released: no per-commit records, no names, no e-mail addresses, no commit messages and no identity hashes. Author and committer identities were salted and hashed at ingest only to count distinct authors per repository-month. The survival and size samples are one row per sampled pull request or commit, with the repository and month but no PR number and no commit SHA. Do not use this dataset to profile individual developers.

Files

path	content
`aggregated/repo_month.parquet`	commits, attributed commits, active and attributing authors per repository-month, six specifications, with and without shared-history deduplication
`aggregated/repo_month_tool.parquet`	attributed commits per repository-month and tool
`aggregated/reverts.parquet`	window commits and later reverts per repository-month and attribution status
`data/survival.csv`	sampled merged pull requests (no PR numbers): merge method, attribution on head commits and on the landed commit
`data/size_sample.csv`	2,000 sampled commits (no SHAs): month, attribution, files changed, insertions, deletions
`data/policy.csv`, `configs/policy_overrides.yaml`	AI-contribution policy class per repository, with hand corrections
`data/manifest.json`	snapshot date, head SHA, default branch, commit counts, git version per repository
`configs/`	frozen signature catalogue, bot list, repository lists; `data/s3_match.json` matching output
`SHA256SUMS`	checksums of every file above

Method

Pre-registered (PREREGISTRATION.md, tag prereg-v1); deviations in DEVIATIONS.md. Code: the coauthored package (Apache-2.0). The paper's tables and figures rebuild from these files; the aggregates themselves rebuild from a fresh scan of the public repositories (REPRODUCE.md).

Licence

CC BY 4.0. Commit metadata derives from public repositories under their own licences.