Large Language Model (LLM) Agents are advancing quickly, with the increasing leveraging of LLM Agents to assist in development tasks such as code generation. While LLM Agents accelerate code generation, studies indicate they may introduce adverse effects on development. However, existing metrics solely measure pass rates, failing to reflect impacts on long-term maintainability and readability, and failing to capture human intuitive evaluations of PR. To increase the comprehensiveness of this problem, we investigate and evaluate the characteristics of LLM to know the pull requests' characteristics beyond the pass rate. We observe the code quality and maintainability within PRs based on code metrics to evaluate objective characteristics and developers' reactions to the pull requests from both humans and LLM's generation. Evaluation results indicate that LLM Agents frequently disregard code reuse opportunities, resulting in higher levels of redundancy compared to human developers. In contrast to the quality issues, our emotions analysis reveals that reviewers tend to express more neutral or positive emotions towards AI-generated contributions than human ones. This disconnect suggests that the surface-level plausibility of AI code masks redundancy, leading to the silent accumulation of technical debt in real-world development environments. Our research provides insights for improving human-AI collaboration.

翻译：大型语言模型（LLM）智能体正快速发展，越来越多地被用于辅助代码生成等开发任务。尽管LLM智能体加速了代码生成，但研究表明它们可能对开发产生负面影响。然而，现有指标仅衡量通过率，未能反映其对长期可维护性和可读性的影响，也未能捕捉人类对拉取请求（PR）的直观评价。为更全面地探究此问题，我们调查并评估了LLM的特性，以了解超越通过率的拉取请求特征。我们基于代码度量指标观察PR中的代码质量与可维护性，以评估客观特征，并分析开发者对人类和LLM生成代码的拉取请求的反应。评估结果表明，与人类开发者相比，LLM智能体经常忽视代码复用机会，导致更高的冗余度。与质量问题形成对比的是，我们的情感分析显示评审者对AI生成的代码贡献往往表现出更中性或更积极的情感倾向。这种脱节表明AI代码的表面合理性掩盖了冗余问题，导致技术债务在实际开发环境中悄然累积。本研究为改进人机协作提供了见解。