超越局部代码优化：软件系统优化的多智能体推理
Beyond Local Code Optimization: Multi-Agent Reasoning for Software System Optimization
AI总结：
本文提出多智能体框架，通过整合控制流、数据流与架构依赖信号，实现微服务系统的整体优化，提升吞吐量和响应时间。
AI中文摘要：
大型语言模型和AI代理近期在自动化软件性能优化中展现出潜力，但现有方法主要依赖局部、语法驱动的代码转换，限制了对程序行为和系统性能交互的推理能力。本文探讨了微服务系统的整体优化可行性，引入多智能体框架整合控制流、数据流表示与架构及跨组件依赖信号，支持系统级性能推理。所提系统分解为协同的智能体角色——总结、分析、优化和验证——共同识别跨切瓶颈并构建跨软件栈的多步骤优化策略。我们在一个基于微服务的系统上展示了证明概念，实现了36.58%的吞吐量提升和27.81%的平均响应时间减少。
英文摘要：
Large language models and AI agents have recently shown promise in automating software performance optimization, but existing approaches predominantly rely on local, syntax-driven code transformations. This limits their ability to reason about program behavior and capture whole system performance interactions. As modern software increasingly comprises interacting components - such as microservices, databases, and shared infrastructure - effective code optimization requires reasoning about program structure and system architecture beyond individual functions or files. This paper explores the feasibility of whole system optimization for microservices. We introduce a multi-agent framework that integrates control-flow and data-flow representations with architectural and cross-component dependency signals to support system-level performance reasoning. The proposed system is decomposed into coordinated agent roles - summarization, analysis, optimization, and verification - that collaboratively identify cross-cutting bottlenecks and construct multi-step optimization strategies spanning the software stack. We present a proof-of-concept on a microservice-based system that illustrates the effectiveness of our proposed framework, achieving a 36.58% improvement in throughput and a 27.81% reduction in average response time.
