Add proper dataset card (bundle table, GitHub link, citation)

README.md

@@ -1,3 +1,156 @@
 ---
 license: cc-by-4.0
+pretty_name: "Debt Behind the AI Boom"
+language:
+  - en
+tags:
+  - code
+  - software-engineering
+  - technical-debt
+  - ai-generated-code
+  - empirical-study
+  - static-analysis
+  - github
+size_categories:
+  - 100K<n<1M
+configs:
+  - config_name: repos
+    data_files: ai_repos.csv
 ---
+
+# 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](https://arxiv.org/abs/2603.28592) · 💻 Code: [github.com/yueyueL/tech-debt-ai-coding](https://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**
+
+---
+
+## 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.
+
+---
+
+## How to download
+
+These files are tracked with Git LFS — use the `huggingface_hub` client so downloads
+are counted and resume automatically if interrupted:
+
+```bash
+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:
+
+```python
+from huggingface_hub import hf_hub_download
+path = hf_hub_download("yueyuel/tech-debt-ai-coding", "results-out.zip", repo_type="dataset")
+```
+
+---
+
+## Reproduction tiers
+
+The study reproduces at three levels of effort. Full step-by-step commands are in the
+[GitHub README](https://github.com/yueyueL/tech-debt-ai-coding#reproduction-tiers).
+
+| 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:**
+```bash
+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:**
+```bash
+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
+```
+
+---
+
+## Schema
+
+Brief notes below; full schemas (including the commit and metric JSON shapes) are in
+[`data/README.md`](https://github.com/yueyueL/tech-debt-ai-coding/blob/main/data/README.md) on GitHub.
+
+**`focused_repos.json`**
+```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`)
+```json
+{
+  "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"}
+  ]
+}
+```
+
+---
+
+## License
+
+Released under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). You are free
+to share and adapt the data with attribution to the paper below.
+
+## Citation
+
+```bibtex
+@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}
+}
+```