pith:FJMALU2F
A Large-Scale Comprehensive Measurement of AI-Generated Code in Real-World Repositories
AI-generated code in real-world repositories differs from human-written code in complexity, structure, and post-commit evolution.
arxiv:2603.27130 v3 · 2026-03-28 · cs.SE
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\usepackage{pith}
\pithnumber{FJMALU2FFM5H3PLFL3EEU3WHFK}
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Claims
Our study provides a comprehensive view of the characteristics of AI-generated code in practice and highlights how AI-assisted development differs from conventional human-driven development.
The detection pipeline that combines heuristic filtering with LLM-based classification accurately identifies AI-generated code at scale with acceptable error rates.
A large-scale study of real-world repositories finds that AI-generated code differs from human-written code in complexity, structural traits, defect indicators, and commit-level activity patterns.
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| First computed | 2026-07-03T00:16:53.361014Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
2a5805d3452b3a7dbd655ec84a6ec72a80057f0305b517dcfac4cc1e63691376
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/FJMALU2FFM5H3PLFL3EEU3WHFK \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 2a5805d3452b3a7dbd655ec84a6ec72a80057f0305b517dcfac4cc1e63691376
Canonical record JSON
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