Performance optimization is a critical yet challenging aspect of software development, often requiring a deep understanding of system behavior, algorithmic tradeoffs, and careful code modifications. Although recent advances in AI coding agents have accelerated code generation and bug fixing, little is known about how these agents perform on real-world performance optimization tasks. We present the first empirical study comparing agent- and human-authored performance optimization commits, analyzing 324 agent-generated and 83 human-authored PRs from the AIDev dataset across adoption, maintainability, optimization patterns, and validation practices. We find that AI-authored performance PRs are less likely to include explicit performance validation than human-authored PRs (45.7\% vs. 63.6\%, $p=0.007$). In addition, AI-authored PRs largely use the same optimization patterns as humans. We further discuss limitations and opportunities for advancing agentic code optimization.

翻译：性能优化是软件开发中至关重要却又极具挑战性的环节，通常需要对系统行为、算法权衡有深入理解，并辅以细致的代码修改。尽管近年来AI编程智能体在代码生成和错误修复方面取得了显著进展，但人们对于这些智能体在真实世界性能优化任务上的表现知之甚少。我们首次开展了比较智能体与人类编写的性能优化提交的实证研究，分析了来自AIDev数据集的324个智能体生成和83个人类编写的拉取请求，涵盖采纳度、可维护性、优化模式及验证实践等多个维度。研究发现，与人类编写的性能优化拉取请求相比，AI编写的性能优化拉取请求包含明确性能验证的可能性更低（45.7% 对比 63.6%，$p=0.007$）。此外，AI编写的拉取请求主要采用了与人类相同的优化模式。我们进一步讨论了当前方法的局限性以及推动智能体代码优化发展的机遇。