Problem
Research questions and friction points this paper is trying to address.
coding agents
technical debt
development velocity
AI configuration
code quality
A Few Pages of Markdown: Committed AI Configuration and Lower Quality Cost after Coding-Agent Adoption
🤖 AI Summary
研究通过引入RAMP模型来评估团队配置AI工具的成熟度，发现采用编码代理虽加速开发，但未妥善配置AI会导致代码质量下降。
📝 Abstract
Coding agents increase development velocity but also technical debt. Prior work reports only average effects across adopters, hiding wide differences between teams. We introduce RAMP (Repository AI Maturity Profile), a four-level cumulative maturity model grounded in version-controlled artifacts that teams commit to configure AI tools. RAMP runs from behavioral rules and coding standards through named agent definitions to multi-agent orchestration, with observed practice concentrated in the first three levels. Across 441 repositories the levels behave as a cumulative scale, and independent human annotation reproduces RAMP's repository-level labels on 97% of a held-out sample. Adoption is cumulative, forward-only, and set-and-forget: 73.8% of artifacts are committed once and never modified. Re-estimating an existing agent-adoption panel within each stratum, agents accelerate development regardless of maturity (28-38% more commits), but quality diverges: among agent-first repositories, where the contrast is identified, those without committed AI configuration show roughly twice the increase in cognitive complexity (+53% versus +27%) and 1.7x the increase in static-analysis warnings. Because maturity is observational, correlated engineering discipline or model capability may explain part of the gap; we present these findings as hypothesis-generating and release RAMP as a reusable instrument.
Innovation
Methods, ideas, or system contributions that make the work stand out.
RAMP
cumulative maturity model
coding-agent adoption
technical debt
cognitive complexity
🔎 Similar Papers
No similar papers found.
