arXiv:2606. 28235v1 Announce Type: cross Abstract: Autonomous coding agents now open and merge pull requests in shared repositories at scale, and the field evaluates them the way it has always evaluated components, one agent at a time, on isolated benchmark tasks.
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.
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.
The study evaluates 26,999 pull requests that modify both application code and deployment infrastructure, using two large datasets and six AI agent architectures. It finds that such dual‑scope changes occur in only about 4.5% of agent‑generated PRs but lead to two distinct integration outcomes: either significant delays due to CI/CD and provisioning checks, or a high rate of silent merges with reduced human review. The results highlight that current AI agents lack deployment awareness, causing unverified dependencies to slip into production pipelines.
arXiv:2608.29204v2 Announce Type: replace-cross Abstract: Coding agents now complete development tasks in real software projects and submit the results as pull requests. A pull request shows the outc...
arXiv:2608.29204v1 Announce Type: cross Abstract: Generative AI-based software engineering agents are becoming routine contributors to real-world software projects. On GitHub, developers can assign t...
Agentic coding tools are increasingly capable of generating and submitting pull requests (PRs) to software projects, introducing new forms of human-agent collaboration in software development. While prior studies have examined PR-level outcomes of agent-generated contributions, less is known about how agentic coding tools are adopted and managed at the project level.
The paper introduces VeeReview, a tool that classifies build‑and‑verify pairs in pull requests using repository signals and a language‑model judge. Evaluated on 1,454 agent‑ and human‑authored pull requests, VeeReview found lower build‑only rates for agent‑authored changes but failed to meet calibration and agreement thresholds. The study concludes that a mechanically decided component and validated human reference are essential for reliable verification‑practice comparisons.
arXiv:2606. 19616v1 Announce Type: cross Abstract: Autonomous coding agents now open millions of pull requests, yet large-scale studies find their PRs are produced faster but accepted less often - a coordination and trust gap that pull-request-level telemetry cannot explain.
arXiv:2607. 14037v1 Announce Type: cross Abstract: Agentic coding tools are increasingly capable of generating and submitting pull requests (PRs) to software projects, introducing new forms of human-agent collaboration in software development.
arXiv:2606. 13449v1 Announce Type: cross Abstract: AI-agents (e.
arXiv:2607. 09902v1 Announce Type: cross Abstract: Agentic coding tools are increasingly used to make autonomous repository-level changes to real-world projects.