yueyuel/tech-debt-ai-coding

Debt Behind the AI Boom — Replication Data

Data for the paper:

Debt Behind the AI Boom: A Large-Scale Empirical Study of AI-Generated Code in the Wild Yue Liu, Ratnadira Widyasari, Yanjie Zhao, Ivana Clairine Irsan, Junkai Chen, David Lo 📄 arXiv:2603.28592 · 💻 Code: github.com/yueyueL/tech-debt-ai-coding

We mined 302.6K AI-authored commits from 6,299 GitHub repositories across five AI coding assistants (GitHub Copilot, Claude, Cursor, Gemini, Devin), ran static analysis before and after each commit, and tracked whether the introduced issues still survive in the codebase today.

Key finding: 484K issues were introduced; 22.7% still survive at HEAD, including issues introduced more than nine months earlier.

This Hugging Face repository hosts the data bundles. All code lives on GitHub: 👉 https://github.com/yueyueL/tech-debt-ai-coding

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What's in this dataset

File	Size	Uncompressed	What it is
focused_repos.json	1.6 MB	—	Manifest of the 6,299 analyzed repositories (stars, language, AI-commit counts per repo). The authoritative input to the analysis pipeline.
ai_repos.csv	0.55 MB	—	Repo metadata (stars, language, primary AI tool) for the same 6,299 repos. (Browsable in the Dataset Viewer above.)
commits.zip	48 MB	~265 MB	All 6,299 <repo>_commits.json files (the AI-authored commit lists). Input to reproduction Tier 3.
results-out.zip	978 MB	~17 GB	Per-repo analyzer output (debt_metrics.json, issue_survival.json, lifecycle_metrics.json, destiny_metrics.json, summary.json + debug/) for all 6,299 repos. Input to reproduction Tier 2.

focused_repos.json and ai_repos.csv are also in the GitHub repo; they are mirrored here so this dataset is self-contained. The two .zip bundles are only available here.

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How to download

These files are tracked with Git LFS — use the huggingface_hub client so downloads are counted and resume automatically if interrupted:

pip install -U huggingface_hub

# A single bundle
hf download yueyuel/tech-debt-ai-coding results-out.zip --repo-type dataset --local-dir .
hf download yueyuel/tech-debt-ai-coding commits.zip     --repo-type dataset --local-dir .

# Or the entire dataset
hf download yueyuel/tech-debt-ai-coding --repo-type dataset --local-dir ./tech-debt-data

In Python:

from huggingface_hub import hf_hub_download
path = hf_hub_download("yueyuel/tech-debt-ai-coding", "results-out.zip", repo_type="dataset")

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Reproduction tiers

The study reproduces at three levels of effort. Full step-by-step commands are in the GitHub README.

Tier	What you do	Time	Disk	Needs
T1	Browse results/out/aggregate_summary.json via the dashboard	<1 min	0 GB	Python 3.10+ (GitHub clone only — no download)
T2	Re-aggregate from saved per-repo metrics	~5 min	~10 GB	Python 3.10+, results-out.zip
T3	Re-run the full pipeline from raw commits	days	~500 GB	Python 3.10+, Pylint, ESLint, Semgrep, git, commits.zip

T2 — re-aggregate:

hf download yueyuel/tech-debt-ai-coding results-out.zip --repo-type dataset --local-dir .
unzip results-out.zip -d results/
python3 -m src.reporting.aggregate --out-dir results/out

T3 — full re-run:

hf download yueyuel/tech-debt-ai-coding commits.zip --repo-type dataset --local-dir data/
unzip data/commits.zip -d data/
# then follow the GitHub README for analyzer setup + batch run

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Schema

Brief notes below; full schemas (including the commit and metric JSON shapes) are in data/README.md on GitHub.

focused_repos.json

{
  "total_repos": 6299,
  "repos": [
    {"repo": "owner/name", "file": "data/commits/owner_name_commits.json",
     "stars": 12345, "language": "Python", "ai_commits": 42,
     "total_commits": 5000, "ai_percentage": 0.84}
  ]
}

commits/<owner>_<repo>_commits.json (inside commits.zip)

{
  "repo": "owner/name", "total_commits_scanned": 5000, "ai_commits_count": 42,
  "tools_found": {"copilot": 30, "claude": 12},
  "ai_commits": [
    {"sha": "abc123", "ai_tool": "copilot", "detection_method": "coauthor",
     "date": "2025-06-15T10:30:00Z", "url": "https://github.com/owner/name/commit/abc123"}
  ]
}

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License

Released under CC-BY-4.0. You are free to share and adapt the data with attribution to the paper below.

Citation

@article{liu2026techdebt,
  title         = {Debt Behind the AI Boom: A Large-Scale Empirical Study of
                   AI-Generated Code in the Wild},
  author        = {Liu, Yue and Widyasari, Ratnadira and Zhao, Yanjie and
                   Irsan, Ivana Clairine and Chen, Junkai and Lo, David},
  year          = {2026},
  eprint        = {2603.28592},
  archivePrefix = {arXiv},
  primaryClass  = {cs.SE}
}