GenAI solutions like GitHub Copilot have been shown to increase the productivity of software developers. Yet prior work remains unclear on the quality of code produced and the challenges of maintaining it in software projects. If quality declines as volume grows, technical debt accumulates as experienced developers face increased workloads reviewing and reworking code from less-experienced contributors. We analyze developer activity in Open Source Software (OSS) projects following the introduction of GitHub Copilot. We find that productivity indeed increases. However, the increase in productivity is primarily driven by less-experienced (peripheral) developers. We also find that code written after the adoption of AI requires more rework to satisfy repository standards, indicating a potential increase in technical debt. Importantly, the added rework burden falls on the more experienced (core) developers, who review 6.5% more code after Copilot's introduction, but show a 19% drop in their original code productivity. More broadly, this finding raises caution that productivity gains of AI may mask the growing burden of maintenance on a shrinking pool of experts, together with increased technical debt for the projects. The results highlight a fundamental tension in AI-assisted software development between short-term productivity gains and long-term system sustainability.

翻译：研究表明，GitHub Copilot等生成式人工智能解决方案能够提升软件开发者的生产力。然而，先前研究尚未明确说明所生成代码的质量以及在软件项目中维护此类代码所面临的挑战。若代码质量随数量增长而下降，当经验丰富的开发者需要投入更多工作量审查和重构经验较浅贡献者提交的代码时，技术债务将持续累积。本文分析了GitHub Copilot引入后开源软件项目中开发者的活动情况。研究发现生产力确实有所提升，但这种提升主要来自经验较浅（外围）开发者。同时发现，采用AI辅助编写的代码需要更多返工才能满足代码库标准，这预示着技术债务可能增加。尤为重要的是，额外的返工负担落在了经验更丰富（核心）开发者身上——他们在Copilot引入后需要审查的代码量增加了6.5%，但其原始代码生产力却下降了19%。更广泛而言，这一发现警示我们：人工智能带来的生产力提升可能掩盖了日益增长的系统维护负担正由日益缩减的专家群体承担的现实，同时项目技术债务也在持续增加。研究结果揭示了人工智能辅助软件开发中短期生产力提升与长期系统可持续性之间的根本矛盾。