---
pretty_name: "qmmit Agent-Commit Benchmark v3 - 2000 OSS repositories"
language:
   - en
   - code
license: other
license_name: qmmit-dataset-terms
license_link: LICENSE.md
annotations_creators:
   - machine-generated
language_creators:
   - found
source_datasets:
   - original
task_categories:
   - tabular-classification
   - tabular-regression
size_categories:
   - 1K<n<10K
tags:
   - git
   - commit-metadata
   - mining-software-repositories
   - msr
   - empirical-software-engineering
   - code-provenance
   - ai-coding-agents
   - agentic-coding
   - claude-code
   - github-copilot
   - cursor
   - openai-codex
   - devin
   - aider
   - gemini-cli
   - open-source
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*.parquet
dataset_info:
  config_name: default
  features:
    - {name: rank, dtype: int64}
    - {name: repo, dtype: string}
    - {name: github_url, dtype: string}
    - {name: stars_at_collection, dtype: int64}
    - {name: language, dtype: string}
    - {name: ecosystem, dtype: string}
    - {name: window_since, dtype: string}
    - {name: window_label, dtype: string}
    - {name: agent_share_pct_floor, dtype: float64}
    - {name: human_share_pct_ceiling, dtype: float64}
    - {name: commits_in_history, dtype: int64}
    - {name: merge_commits_excluded, dtype: int64}
    - {name: ci_bot_commits_excluded, dtype: int64}
    - {name: commits_considered, dtype: int64}
    - {name: agent_attributed_commits, dtype: int64}
    - {name: human_attributed_commits, dtype: int64}
    - {name: assisted_commits, dtype: int64}
    - {name: autonomous_commits, dtype: int64}
    - {name: assisted_pct, dtype: float64}
    - {name: autonomous_pct, dtype: float64}
    - {name: distinct_agent_authors, dtype: int64}
    - {name: top_author_share_of_agent_commits, dtype: float64}
    - {name: adoption_pattern, dtype: string}
    - {name: low_activity, dtype: bool}
    - {name: agents_json, dtype: string}
    - {name: timeline_json, dtype: string}
    - {name: agent_commit_samples_json, dtype: string}
  splits:
    - name: train
      num_examples: 2000
---

# Repository-Level Measurement of Self-Declared Coding-Agent Commit Signatures

**Dataset release:** `2026-09-18-v3.0`  
**Schema:** `3.0.0`
**Release stamp:** dataset `2026-09-18-v3.0` · ruleset `sha256:b2e8889c66f72c18a61839f0bf1a9f77b5481ba2def044dba33c797b2f2bdcae` · scanned `2026-09-16`

## Abstract

This dataset contains 2000 repository-level observations from public Git
repositories. Each observation estimates a lower bound on the proportion of
non-merge, non-infrastructure-bot commits that contain a deterministic,
self-declared coding-agent signature during the fixed calendar window
2026-02-01 through 2026-09-15.

The measurement is intentionally narrower than AI-assisted software
development. Git metadata can reveal a signature when an agent participates in
creating a commit; it cannot reveal assistance when a person commits manually
without a signature. Accordingly, all proportions are reported as floors, not
estimates of the amount of code written with AI.

The sample contains 2,236,082 considered commits. The aggregate floor is 8.5%
and the median repository floor is 2%. These are descriptive statistics of
repository histories, not causal estimates and not measurements of individual
developer performance.

## Detector validation

A stratified blind sample of 600 commits (300 detector-positive and 300
detector-negative) was independently reviewed and disagreements were
adjudicated by the human study lead. Weighted precision is **99.45%** (95%
Wilson interval **97.77%–99.86%**), weighted recall is **72.86%**, raw reviewer
agreement is **96.83%**, and Cohen's kappa is **0.938**. Nineteen cases required
adjudication. AI-assisted recommendations used during adjudication are retained
in the private audit trail; the human study lead reviewed and confirmed every
final decision.

## Research questions

The dataset supports the following descriptive questions:

1. What lower-bound share of recent commits contains a coding-agent signature?
2. How often are signed commits associated with assisted versus autonomous
   commit authorship?
3. How concentrated is signed activity across repository contributors?
4. How do these measurements vary across the sampled language ecosystems?

## Files

- `data/train-00000-of-00001.parquet`: the same 2000 observations as a compact
   tabular split for the Hugging Face dataset viewer and programmatic loading.
- `data/repositories.jsonl`: an equivalent newline-delimited copy for
   reproducibility outside the Hub viewer.
- `summary.json`: dataset-level summary, fixed measurement window, method, and
  ecosystem aggregates.
- `repository_resolution.json`: row-by-row canonical URL, HTTP status, default
   branch, and HEAD SHA recorded during the v3 release audit.
- `release_manifest.json`: SHA-256 checksums for the release's canonical data
   and validation artifacts.
- `CORRECTIONS.md`: investigated data-quality reports and their disposition.
- `assets/`: six generated SVG figures used in this card.
- `make_charts.py` and `validate_dataset.py`: reproducible figure generation
   and release validation scripts.

## Data and sampling

Repositories were selected from a public GitHub stars-ranked snapshot of
non-fork, non-archived repositories. The ranking is a collection-time sampling
frame, not a claim that stars measure software quality or represent the entire
open-source population. The final dataset contains 2000 successful scans.

One initially selected repository, `ClickHouse/ClickHouse`, could not be
cloned reliably in the collection environment and was replaced by
`home-assistant/frontend`. The replacement is recorded in the collection
workflow; no missing observation was imputed.

### Repository identity audit

Before the v3 release, every one of the 2000 `github_url` values was resolved
through GitHub and independently checked with `git ls-remote --symref`. All 2000
returned HTTP 200, exposed a readable HEAD SHA, matched their `repo` slug, and
resolved to 2000 unique canonical targets. The complete audit—not just its
summary—is published in `repository_resolution.json`.

The audit found one renamed source repository and records its canonical target
in the resolution manifest. No two observations resolve to the same canonical
repository.

## Operational definitions and method

The scanner reads Git commit metadata and applies literal signature rules for
Claude Code, Cursor, GitHub Copilot, Devin, OpenAI Codex, Aider, Gemini CLI,
and other supported agent signatures. It does not use code-style heuristics,
machine-learning authorship classifiers, or perplexity scoring.

```text
considered commits = all commits - merge commits - CI/dependency bot commits
agent-share floor  = agent-attributed commits / considered commits
```

Agent detection runs before generic `[bot]` exclusion because some coding
agents are GitHub Apps and use bot identities. Matched commits are split into:

- `assisted`: a human identity authored the commit and the agent signed it;
- `autonomous`: a bot identity authored the commit.

The dataset also reports concentration: distinct agent authors, the largest
author's share of agent commits, and an adoption pattern (`none`, `individual`,
`team`, or `org-wide`). Identities themselves are never exported.

### Outcome variables

- `agent_share_pct_floor`: detected agent-attributed commits divided by
   considered commits.
- `assisted_pct` and `autonomous_pct`: the two commit-authorship modes divided
   by considered commits.
- `distinct_agent_authors`, `top_author_share_of_agent_commits`, and
   `adoption_pattern`: concentration measures based on internal author counts.
- `low_activity`: a quality flag for repositories with fewer than 100
   considered commits. These observations remain in the dataset but should be
   treated as high-variance measurements.

All percentages are rounded to one decimal place by the scanner.

Agent-category commit counts are not necessarily mutually exclusive: a single
commit may match more than one literal signature rule. The unique
`agent_attributed_commits` field remains the denominator-safe primary count.

## Descriptive figures

### Ecosystem spread

![Agent-share floor by ecosystem](./assets/01-ecosystems.svg)

### Adoption patterns

![Adoption patterns](./assets/02-adoption-patterns.svg)

### Commit composition

![Commit composition](./assets/03-commit-composition.svg)

### Distribution of repository floors

![Floor distribution](./assets/04-floor-distribution.svg)

### Monthly curve

![Monthly signed share](./assets/05-monthly-curve.svg)

### Share and concentration

![Share versus concentration](./assets/06-share-vs-concentration.svg)

Generate the figures and validate the release with:

```bash
python make_charts.py --parquet data/train-00000-of-00001.parquet --summary summary.json --out assets
python validate_dataset.py data/train-00000-of-00001.parquet summary.json repository_resolution.json
```

## Collection and reproducibility

The reports were generated by `qmmit-cli` using blobless Git clones and the
fixed `--since 2026-02-01` window. The public site and scanner source are in
the project repositories:

- https://github.com/pandey019/qmmit-cli
- https://qmmit.dev

The exported rows contain repository URLs, collection-time language and star
metadata, aggregate counts, monthly timelines, agent categories, and public
commit hash/subject samples. The raw clone directories and raw scanner
artifacts are not part of the published dataset.

## Interpretation

The aggregate floor should not be interpreted as the prevalence of AI-written
code. A low or zero value is compatible with substantial unobserved AI
assistance if developers commit without agent signatures. Conversely, a high
value can reflect the workflow of a small number of contributors. The
concentration fields are therefore necessary for interpreting the share.

The sample is not a random sample of all repositories. Repository age,
activity, popularity, language, and Git workflow may affect both inclusion and
the probability that a signature remains in history. Comparisons across
ecosystems are descriptive and should not be treated as causal comparisons.

## Validity threats and limitations

1. This is a lower bound. Squash merges, rebases, and manual commits can strip
   signatures.
2. A zero result means no matching signature was observed, not that AI was not
   used.
3. Commit counts are not lines of code; a tiny fix and a large refactor each
   count as one commit.
4. Low-activity repositories are included for coverage and marked with
   `low_activity`; their percentages are noisy.
5. Repository popularity and language metadata are collection-time context,
   not causal variables.
6. Do not use this dataset to rank, surveil, or identify individual developers.

The signature rules are versioned with the scanner but are not a complete
catalogue of all coding agents. Squash merges and rebases can remove trailers;
manual commits can leave no signal. Commit counts also weight a one-line fix
and a large refactor equally.

## Ethics and privacy

Contributor names and email addresses are used transiently by the scanner for
aggregate concentration calculations and are deliberately excluded from the
export. The dataset must not be joined with external identity data to produce
individual-level rankings. Public commit subjects and hashes are included only
as reproducibility samples and may contain third-party text.

Public commit subjects and hashes are included only as reproducibility samples.
They may contain repository-authored text and remain subject to the source
repository's license and terms. Users must independently review source-repo
licenses before redistributing those fields.

## Citation

```bibtex
@dataset{qmmit_agent_commit_index_2026,
   title = {Repository-Level Measurement of Self-Declared Coding-Agent Commit Signatures},
   author = {Pandey, Balram},
  year = {2026},
      version = {2026-09-18-v3.0},
   url = {https://huggingface.co/datasets/balrampandey/qmmit-open-source-agent-commit-index}
}
```

## License

The qmmit code is MIT-licensed. This dataset is a collection of derived
repository-level measurements plus public repository metadata and sample commit
fields. No single license is asserted over third-party repository content;
please observe each source repository's license and GitHub terms when using or
redistributing the samples.