Generative AI coding agents are entering the open-source supply chain, yet their diverse and often invisible traces leave their prevalence poorly understood. We introduce a multi-layered detection framework that integrates configuration-file scanning, commit-message analysis, author-identity matching, and bot-signature lookup across World of Code (180M+ Git repositories), classifying agent traces into four behavioral types.
arXiv:2606. 24429v1 Announce Type: cross Abstract: Generative AI coding agents are entering the open-source supply chain, yet their diverse and often invisible traces leave their prevalence poorly understood.
By Arsham Khosravani, Audris Mockus
The paper audits two single‑investigator AI‑assisted research repositories to assess how well they record provenance of AI tool use. One repository contains a prescriptive trace of rule files, with 85.6% of committed lines still visible, while the other holds a narrative trace of an agent’s scratchpad, showing only 21.8% visibility. The study finds that repository‑level indicators such as the Co‑Authored‑By trailer are inconsistent and unreliable for measuring AI involvement, and it outlines which provenance claims each trace type can support or fail to support.
By Koutian Wu, Zesen Huang, Hejia Geng
arXiv:2606. 18168v1 Announce Type: cross Abstract: Software practitioners increasingly use AI coding agents that generate test code alongside production code in open source pull requests (PRs).
By Dipayan Banik, Kowshik Chowdhury, Shazibul Islam Shamim
arXiv:2609.08789v1 Announce Type: cross
Abstract: Frontier AI developers publish safety frameworks that commit them to evidencing whether their models are dangerous. The European Union and California...
By Louis Yiven Zhu
arXiv:2509.21891v3 Announce Type: replace-cross
Abstract: Fine-tuning large language models for code editing has typically relied on mining commits and pull requests. The working hypothesis has been...
By Yangtian Zi, Zixuan Wu, Aleksander Boruch-Gruszecki, Jonathan Bell, Arjun Guha